Paper Citation Record · LEDGER

A Scoping Review of Deep Learning Methods for Photoplethysmography Data

As of 10 August 2026, this Paper Citation Record lists 100 of 296 outbound references and 4 inbound Pith citation observations for arXiv:2401.12783.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.12783 v3

Coverage vector

measured 100 of 296 reference resolution

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One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:21.168678Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-04T07:29:38.765611Z

Reference resolution

100 of 296 outbound references displayed

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External citation measurements

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Outbound references

Observation af7f653b-d32d-4bf4-8f5c-c179dda52ea4 · outbound

This paper cites Deep PPG: Large-scale heart rate estimation with convolu- tional neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep PPG: Large-scale heart rate estimation with convolu- tional neural networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.200149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 24e05597-4361-4fab-841d-faef0db26c00 · outbound

This paper cites Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.768159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79ddc2e9-5b9d-420b-a723-3c39d2cabb45 · outbound

This paper cites Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.898596Z

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Observation 2fa04a1c-b24d-4ed4-8b6f-761768feea0a · outbound

This paper cites Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.205607Z

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Observation 255fcc73-c512-4f5b-a11d-3d6cdeb25a47 · outbound

This paper cites Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.131780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb9d6a86-d0f5-46dc-891b-a3c52740c1de · outbound

This paper cites Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.210080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41260207-711c-45c8-bbba-bea2ca55b7f3 · outbound

This paper cites Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.550394Z

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Observation e3ab4f0b-b7af-4535-b217-75474c93f2bc · outbound

This paper cites Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.464147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 270de2fd-4907-4434-b7cd-67b4a55a54be · outbound

This paper cites Application of photoplethysmography signals for healthcare systems: An in-depth review.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Application of photoplethysmography signals for healthcare systems: An in-depth review

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.812675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f24cb1e-2bdc-4fbe-a07b-d655036ffcff · outbound

This paper cites Photoplethysmography based atrial fibrillation detection: a review.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Photoplethysmography based atrial fibrillation detection: a review

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.827752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4a29303e-4f7a-4ee8-8ecf-f9166a374fc6 · outbound

This paper cites A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.858722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d1ac16d1-a68c-450a-950f-0590d31dca2d · outbound

This paper cites A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.803300Z

Source-reported events for the cited work

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Observation 40a6a7ec-8d2b-4bfb-b8c4-a618eb64b454 · outbound

This paper cites Photoplethysmography—new applications for an old technology: a sleep technology review.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Photoplethysmography—new applications for an old technology: a sleep technology review

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.457706Z

Source-reported events for the cited work

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Observation a4b801f4-e848-47ba-9595-a76d93fde322 · outbound

This paper cites A review of wearable multi-wavelength photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A review of wearable multi-wavelength photoplethysmography

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.451746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7bf4d95-28bf-4185-a93c-6c1265bfa240 · outbound

This paper cites The current state of optical sensors in medical wearables.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data The current state of optical sensors in medical wearables

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.787650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ddb6104-d763-4e6c-839a-2564cfa946c0 · outbound

This paper cites MW-PPG sensor: An on-chip spectrometer approach.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data MW-PPG sensor: An on-chip spectrometer approach

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.703563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f3b5f2ce-bf37-4440-8ba0-d18d9fee92b0 · outbound

This paper cites Estimation of absolute blood pressure using video images captured at different heights from the heart.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Estimation of absolute blood pressure using video images captured at different heights from the heart

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.302231Z

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Observation 7d6d20c6-8a2f-45dd-b125-9cf007713929 · outbound

This paper cites An applicable approach for extracting human heart rate and oxygen saturation during physical movements using a multi-wavelength illumination optoelectronic sensor system.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data An applicable approach for extracting human heart rate and oxygen saturation during physical movements using a multi-wavelength illumination optoelectronic sensor system

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.058305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a2eb6ad-c1f6-4517-b92b-de450bd677a4 · outbound

This paper cites Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.140232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e1914487-1628-41c9-98f1-dd638632715f · outbound

This paper cites Validity and reliability of the Apple Watch for measuring heart rate during exercise.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Validity and reliability of the Apple Watch for measuring heart rate during exercise

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.034847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5aedb67-9566-4bdd-af4b-e426022ae554 · outbound

This paper cites Investigating sources of inaccuracy in wearable optical heart rate sensors.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Investigating sources of inaccuracy in wearable optical heart rate sensors

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.122772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ed26a04-d029-427c-be87-5735697bc208 · outbound

This paper cites The Apple Watch spO2 sensor and outliers in healthy users.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data The Apple Watch spO2 sensor and outliers in healthy users

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.889886Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aef592c2-3642-4576-b40f-fc4b1f318ec0 · outbound

This paper cites Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:36:02.219651Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e52b6c09-c6f4-4eaf-99a6-2f49a57ee368 · outbound

This paper cites Multi-night validation of a sleep tracking ring in adolescents compared with a research actigraph and polysomnography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Multi-night validation of a sleep tracking ring in adolescents compared with a research actigraph and polysomnography

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.078847Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f60d38aa-aa11-45e3-90f0-5f41df8cffa1 · outbound

This paper cites Deep learning.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.884921Z

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Observation 74a16e29-7577-433b-ab95-980d0e9da435 · outbound

This paper cites The regression analysis of binary sequences.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data The regression analysis of binary sequences

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.783700Z

Source-reported events for the cited work

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Observation 08a3ca3f-70cf-40d8-9e60-9aaebb4b60f9 · outbound

This paper cites The random subspace method for constructing decision forests.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data The random subspace method for constructing decision forests

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.428230Z

Source-reported events for the cited work

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Observation 9add8ddd-949e-4034-a0fa-34b68cf80d81 · outbound

This paper cites Support-vector networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Support-vector networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.554602Z

Source-reported events for the cited work

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Observation c7f191aa-5d66-45b0-ba18-9afd7a497f19 · outbound

This paper cites Deep learning approaches to detect atrial fibrillation using photoplethysmographic signals: algorithms development study.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning approaches to detect atrial fibrillation using photoplethysmographic signals: algorithms development study

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.422628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5eb7757-1f9c-44f2-a32f-f508ee0faaba · outbound

This paper cites Multiclass arrhythmia detection and classification from photoplethysmography signals using a deep convolutional neural network.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Multiclass arrhythmia detection and classification from photoplethysmography signals using a deep convolutional neural network

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.440273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1935cf5f-ec35-4687-915f-d0b65e015031 · outbound

This paper cites A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.529253Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 082b3e5f-3f45-4ae2-8769-efd8040b7637 · outbound

This paper cites A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG).

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.559324Z

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Observation 6633c927-e5df-4c05-9134-d7d44f32266d · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Imagenet classification with deep convolutional neural networks

Reference 33

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Observation 02fd749e-cae2-404a-bedd-4fafa2503913 · outbound

This paper cites Deep residual learning for image recognition.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep residual learning for image recognition

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.497622Z

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Observation 3ae7b231-8636-48df-89b6-480bc1888932 · outbound

This paper cites Long short-term memory.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Long short-term memory

Reference 35

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Observation 37be9c55-e98c-4a83-8af6-84c25f7fa260 · outbound

This paper cites Learning phrase rep- resentations using RNN encoder-decoder for statistical machine translation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Learning phrase rep- resentations using RNN encoder-decoder for statistical machine translation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.985897Z

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Observation 3657666b-de3a-4547-a81d-4ad9bcd31070 · outbound

This paper cites Attention is all you need.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Attention is all you need

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.981660Z

Source-reported events for the cited work

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Observation 0ddd18eb-4e78-4fbf-9c50-fd0af89be971 · outbound

This paper cites Generative adversarial nets.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Generative adversarial nets

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.663651Z

Source-reported events for the cited work

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Observation 1cbb3fa1-de0f-45e3-9945-991b88d7c32e · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data U-net: Convolutional networks for biomedical image segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:36:02.216139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b55bb21-8221-4534-93ec-f673f6acd947 · outbound

This paper cites ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.070860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46417feb-2d28-42b6-8528-158403d09c02 · outbound

This paper cites Cnn-based deep learning network for human activity recognition during physical exercise from accelerometer and photoplethysmographic sensors.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cnn-based deep learning network for human activity recognition during physical exercise from accelerometer and photoplethysmographic sensors

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.275668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bea348a9-b820-4f52-ba21-ab4678408e0e · outbound

This paper cites Biometric recognition based on scalable end-to-end convolutional neural network using photoplethysmography: A comparative study.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Biometric recognition based on scalable end-to-end convolutional neural network using photoplethysmography: A comparative study

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.266994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 718e3252-7661-4afb-9917-012053a3cd4d · outbound

This paper cites CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.583858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0fc517a-0202-4cc3-978f-3f8b00d422ba · outbound

This paper cites BiometricNet: Deep learning based biometric identification using wrist-worn PPG.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data BiometricNet: Deep learning based biometric identification using wrist-worn PPG

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.046855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b2ad522-1e8e-4c45-86be-d26933312173 · outbound

This paper cites Deep Learning based non-invasive diabetes predictor using Photoplethysmography signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep Learning based non-invasive diabetes predictor using Photoplethysmography signals

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.580100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0f6ba87-be02-483f-98b5-971b423ec974 · outbound

This paper cites Research on estimation of blood glucose based on PPG and deep neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Research on estimation of blood glucose based on PPG and deep neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.171205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66d152cf-f757-494a-b8e8-2b04b1b875a8 · outbound

This paper cites Genetic deep convolutional autoencoder applied for generative continuous arterial blood pressure via photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Genetic deep convolutional autoencoder applied for generative continuous arterial blood pressure via photoplethysmography

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.091226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 828558a9-f7f3-4ebb-9cd7-461ca5f8cc2e · outbound

This paper cites Real-time cuffless continuous blood pressure estimation using deep learning model.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Real-time cuffless continuous blood pressure estimation using deep learning model

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.101205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01c4a685-a9ec-42df-b6c2-7d8231ae27a6 · outbound

This paper cites Prediction of arterial blood pressure waveforms from photoplethys- mogram signals via fully convolutional neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Prediction of arterial blood pressure waveforms from photoplethys- mogram signals via fully convolutional neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.596334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e91dbd93-fb50-4a6c-b51e-49e1b196d662 · outbound

This paper cites PP-Net: A deep learning framework for PPG-based blood pressure and heart rate estimation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data PP-Net: A deep learning framework for PPG-based blood pressure and heart rate estimation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.289606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cad0ac7-6427-49a0-afdb-13e2f070a017 · outbound

This paper cites Personalized blood pressure estimation using photoplethysmography: A transfer learning approach.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Personalized blood pressure estimation using photoplethysmography: A transfer learning approach

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.523615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b8ff3ec-7386-4bae-82b2-89aa72aed781 · outbound

This paper cites Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.105961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f5baf308-4637-4e99-9eac-5b9d2cb8beb1 · outbound

This paper cites Estimating blood pressure trends and the nocturnal dip from photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Estimating blood pressure trends and the nocturnal dip from photoplethysmography

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.383829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c0ecfe0-1313-4906-9121-2b16468f7f38 · outbound

This paper cites Deepcnap: A deep learning approach for continuous noninvasive arterial blood pressure monitoring using photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deepcnap: A deep learning approach for continuous noninvasive arterial blood pressure monitoring using photoplethysmography

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.728614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a4654b9e-c651-43da-a1fe-040ff8062e8a · outbound

This paper cites Deep learning models for the prediction of intraoperative hypotension.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning models for the prediction of intraoperative hypotension

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.736758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7279eacf-e1cc-4db4-9c2f-c284b112b108 · outbound

This paper cites Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.111527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c642eeaf-d67b-4a23-950f-a8b6645380d1 · outbound

This paper cites Cuffless deep learning-based blood pressure estimation for smart wristwatches.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cuffless deep learning-based blood pressure estimation for smart wristwatches

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.679817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dad5a3cf-a6a0-4cb8-bcf9-ad0e8b2c49d0 · outbound

This paper cites Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.338234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3aecd53c-1273-49f2-a5f5-6894db85684f · outbound

This paper cites Cuffless blood pressure estimation from only the waveform of photoplethysmography using CNN.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cuffless blood pressure estimation from only the waveform of photoplethysmography using CNN

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.191271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 06665ee3-9aed-44d4-9244-ad11e72c4d53 · outbound

This paper cites Cuff-less blood pressure estimation from photoplethysmography via visibility graph and transfer learning.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cuff-less blood pressure estimation from photoplethysmography via visibility graph and transfer learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.817243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b061dacb-3f50-4375-9292-28e61035858c · outbound

This paper cites Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.732884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c68a388-db9e-44fa-8e6d-575f874054d9 · outbound

This paper cites Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.162127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5f3f4742-c1d2-4603-8f89-5fa2f5115943 · outbound

This paper cites Using CNN and HHT to predict blood pressure level based on photoplethys- mography and its derivatives.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Using CNN and HHT to predict blood pressure level based on photoplethys- mography and its derivatives

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.226574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42194f84-9bdc-4323-9a5f-6305e58cc31e · outbound

This paper cites Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.195871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9026817-2a05-4fc3-8d48-e012270a441c · outbound

This paper cites An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: A U-Net architecture-based approach.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: A U-Net architecture-based approach

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.720042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c4dbfc4-f6c1-4896-a311-0286ec13c150 · outbound

This paper cites A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.775549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea879a9a-1e65-45b3-9e7b-463267d972d6 · outbound

This paper cites A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.779682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b617580-b4a2-46cb-87f5-caedb65332e1 · outbound

This paper cites A multi-type features fusion neural network for blood pressure prediction based on photoplethys- mography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A multi-type features fusion neural network for blood pressure prediction based on photoplethys- mography

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.832370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5eae646f-0924-4337-833d-0825047abc6a · outbound

This paper cites A deep learning approach to predict blood pressure from ppg signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A deep learning approach to predict blood pressure from ppg signals

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.446974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0e26185-1caf-46c8-9bbc-7a16da480b50 · outbound

This paper cites Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.724196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 07a71cc5-e85d-4be1-a4bb-9e51ac37c1ed · outbound

This paper cites Photoplethysmography and deep learning: enhancing hypertension risk stratification.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Photoplethysmography and deep learning: enhancing hypertension risk stratification

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.692514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 35601c16-d986-46fd-a7b0-ffdaa35b2018 · outbound

This paper cites Features extraction for cuffless blood pressure estimation by autoencoder from photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Features extraction for cuffless blood pressure estimation by autoencoder from photoplethysmography

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.404980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54650d43-a172-4e0b-9cc0-de894601e2bd · outbound

This paper cites Fast emotion recognition based on single pulse PPG signal with convolutional neural network.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Fast emotion recognition based on single pulse PPG signal with convolutional neural network

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.271377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cb1529f-355b-474a-bddc-02e0f62ec503 · outbound

This paper cites Feature augmented hybrid cnn for stress recognition using wrist-based photoplethysmography sensor.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Feature augmented hybrid cnn for stress recognition using wrist-based photoplethysmography sensor

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.183531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b79b8277-b9f7-4895-8973-14f19c65be5f · outbound

This paper cites A deep transfer learning approach for wearable sleep stage classification with photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.253048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7da3bbed-431f-4be8-9da3-8ecc22427599 · outbound

This paper cites Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.906643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 298acdb6-10ee-48a9-ab3d-83f7484c1956 · outbound

This paper cites Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.633683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2108df28-82bf-45bd-973b-7ed385920b53 · outbound

This paper cites SleepPPG-Net: A deep learning algorithm for robust sleep staging from continuous photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data SleepPPG-Net: A deep learning algorithm for robust sleep staging from continuous photoplethysmography

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.257981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4dae9e02-51e1-4094-bc11-6c26795c16ed · outbound

This paper cites Wearable monitoring of sleep- disordered breathing: Estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Wearable monitoring of sleep- disordered breathing: Estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.637743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 824d428b-27fc-4143-b0a2-de8fdc71190f · outbound

This paper cites MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.641603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02be73b2-737f-448a-af59-6386a4607b4f · outbound

This paper cites Cardiogan: Attentive generative adversarial network with dual discriminators for synthesis of ecg from ppg.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Cardiogan: Attentive generative adversarial network with dual discriminators for synthesis of ecg from ppg

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.023360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de107792-714b-45b0-a71a-22ee06d18869 · outbound

This paper cites Reconstructing QRS complex from PPG by transformed attentional neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Reconstructing QRS complex from PPG by transformed attentional neural networks

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.647113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 59b44577-6bee-48bf-ac32-aa9f70cc4816 · outbound

This paper cites P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.652013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e8baafd-4e74-4e48-9543-ad01e1fec1a1 · outbound

This paper cites RespNet: A deep learning model for extraction of respiration from photoplethysmogram.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data RespNet: A deep learning model for extraction of respiration from photoplethysmogram

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.030427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 19a962a3-024e-41f0-b268-78824daa980a · outbound

This paper cites Respiratory rate estimation using PPG: A deep learning approach.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Respiratory rate estimation using PPG: A deep learning approach

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.693697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb046834-d2cc-4674-bd12-d7360c000ed0 · outbound

This paper cites Deep learning for predicting respiratory rate from biosignals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning for predicting respiratory rate from biosignals

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.015038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a15e396-df1b-4211-96b5-0383f487061a · outbound

This paper cites An end-to-end and accurate ppg-based respiratory rate estimation approach using cycle generative adversarial networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data An end-to-end and accurate ppg-based respiratory rate estimation approach using cycle generative adversarial networks

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.616848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27ed06a3-d07d-4a58-aec6-5b3115b6cd1a · outbound

This paper cites A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.612859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation efe66831-b6e0-433e-ad20-4f33f6d4538a · outbound

This paper cites Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.999680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ca632eb-ba52-4f46-8fad-d53ba28f8b59 · outbound

This paper cites Atrial fibrillation classification with smart wearables using short-term heart rate variability and deep convolutional neural networks.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Atrial fibrillation classification with smart wearables using short-term heart rate variability and deep convolutional neural networks

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:36:02.223373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b2dad056-81ed-452d-a75f-b7bd94374869 · outbound

This paper cites Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.148291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4ffa1a1-4195-418d-87c5-e69bf0ec4587 · outbound

This paper cites Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.348814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1eb55ab2-22ea-4259-9455-88f811db79bc · outbound

This paper cites Deep learning for heart rate estimation from reflectance photoplethysmography with acceleration power spectrum and acceleration intensity.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning for heart rate estimation from reflectance photoplethysmography with acceleration power spectrum and acceleration intensity

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:36:02.230553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5f510383-23ff-4324-b7a5-c374f534c2f8 · outbound

This paper cites Deep learning-based photoplethysmography classification for peripheral arterial disease detection: A proof-of-concept study.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deep learning-based photoplethysmography classification for peripheral arterial disease detection: A proof-of-concept study

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:36:02.204960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5b595c8-529c-4a88-bdca-25c87193f23d · outbound

This paper cites Deepheart: A deep learning approach for accurate heart rate estimation from ppg signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Deepheart: A deep learning approach for accurate heart rate estimation from ppg signals

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:54.871526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c56b850-bcfd-432d-b333-f0628db4db36 · outbound

This paper cites Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.239678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c51eb516-b1e4-43f4-912a-e04d8628739c · outbound

This paper cites Multi-task deep learning for cardiac rhythm detection in wearable devices.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Multi-task deep learning for cardiac rhythm detection in wearable devices

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.086624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ba7b7084-826a-4109-8419-d3113b7c0aaf · outbound

This paper cites PPGnet: Deep network for device independent heart rate estimation from photoplethysmogram.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data PPGnet: Deep network for device independent heart rate estimation from photoplethysmogram

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.244042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b356b602-7cbe-4a5e-b71d-986897ebed52 · outbound

This paper cites Prediction of vascular aging based on smartphone acquired PPG signals.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Prediction of vascular aging based on smartphone acquired PPG signals

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.218102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 588675ad-7a60-4a4a-8b9d-9c4dec984678 · outbound

This paper cites Q-ppg: Energy-efficient ppg-based heart rate monitoring on wearable devices.

A Scoping Review of Deep Learning Methods for Photoplethysmography Data Q-ppg: Energy-efficient ppg-based heart rate monitoring on wearable devices

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T04:33:55.233704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation f0068cf0-ce8c-487b-ac1b-c0a4104ed412 · inbound

Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers cites this paper.

Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers A Scoping Review of Deep Learning Methods for Photoplethysmography Data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.168678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35f90c03-de93-4610-8339-10d665977b61 · inbound

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion cites this paper.

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion A Scoping Review of Deep Learning Methods for Photoplethysmography Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:39.613029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04a7bf23-369a-49b1-b149-10f757896f41 · inbound

AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling cites this paper.

AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling A Scoping Review of Deep Learning Methods for Photoplethysmography Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T00:19:30.658373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 878ef69f-4922-4764-accf-5d991164abe8 · inbound

Pixel Watch: Robust Heart Rate Sensing from Multipath PPG and On-Device Deep Learning Trained on 10,000 hours of Free-Living and Fitness Data cites this paper.

Pixel Watch: Robust Heart Rate Sensing from Multipath PPG and On-Device Deep Learning Trained on 10,000 hours of Free-Living and Fitness Data A Scoping Review of Deep Learning Methods for Photoplethysmography Data

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T07:29:38.767069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.