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Causal Inference for Hypertension Prediction With Wearable Electrocardiogram and Photoplethysmogram Signals: Feasibility Study

Causal Inference for Hypertension Prediction With Wearable Electrocardiogram and Photoplethysmogram Signals: Feasibility Study

Hence, there are data-driven approaches based on noninvasive signals for the detection of hypertension, such as electrocardiogram (ECG) or photoplethysmogram (PPG), that are easily accessible from wearable sensors [2]. Subsequently, wearable monitoring can continuously monitor patients’ physiological conditions 24 hours a day.

Ke Gong, Yifan Chen, Xinyue Song, Zhizhong Fu, Xiaorong Ding

JMIR Cardio 2025;9:e60238


Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

DIA: diastolic; INC: incident wave; INF: inflection point; OPPG: original photoplethysmogram; PPG: photoplethysmogram; REF: reflected wave; RPPG: reconstructed photoplethysmogram; SYS: systolic. The features for vascular aging assessment consist of a basic feature defined from the specific points of the waveform before and after the decomposition of the incident and reflected waves of the PPG and a combined feature generated by combining the basic feature.

Junyung Park, Hangsik Shin

JMIR Med Inform 2022;10(3):e33439


Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

A photoplethysmogram (PPG) is a biosignal that can be obtained continuously and noninvasively using a pulse oximeter. Because a PPG conveys much information about a patient’s condition, many attempts have been made to quantify pain by analyzing PPG signals [3,5-7]. The surgical pleth index (SPI; GE Healthcare), developed for quantifying nociception during general anesthesia, only considers the amplitude and heartbeat interval of a PPG [3].

Byung-Moon Choi, Ji Yeon Yim, Hangsik Shin, Gyujeong Noh

J Med Internet Res 2021;23(2):e23920


Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

This is possible because breathing periodicity [6,7] and effort [8] modulate photoplethysmogram (PPG) amplitude, frequency, and baseline wander [9,10]. Filtering and feature-based signal processing approaches can be applied to the PPG signal to extract a surrogate respiratory signal. This in turn can be processed to derive breathing rate (BR) with varying degrees of accuracy [7].

Joseph Prinable, Peter Jones, David Boland, Cindy Thamrin, Alistair McEwan

JMIR Mhealth Uhealth 2020;8(7):e13737


In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study

In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study

The seat incorporates a single-lead ECG for measuring the electrical activity of the heart and as a reference for ensemble averaging [24], a ballistocardiogram (BCG) for measuring the mechanical forces associated with the cardiac cycle, and a photoplethysmogram (PPG) for measuring Sp O2 and pulse transit time (PTT) (Figure 2).

Nicholas J Joseph Conn, Karl Q Schwarz, David A Borkholder

JMIR Mhealth Uhealth 2019;7(1):e12419