Journal ArticleDOI
On-Board Signal Quality Assessment Guided Compression of Photoplethysmogram for Personal Health Monitoring
TLDR
In this paper, an on-board pulse signal quality assessment (SQA) before transmission can save the battery life of the wearable device for portable health monitoring applications, which is a popular diagnostic tool for the assessment of various cardiovascular functions.Abstract:
Photoplethysmography (PPG) is a popular diagnostic tool for the assessment of various cardiovascular functions. Under continuous ambulatory measurements, PPG data get corrupted due to motion artifact (MA). Thus, on-board pulse signal quality assessment (SQA) before transmission can save the battery life of the wearable device for portable health monitoring applications. This article describes an SQA guided compression (SQAGC) of PPG data using a modified gain-shaped vector quantization (GSVQ) technique. The SQA was performed using kurtosis and autocorrelation to generate a binary classification rule to detect good quality pulses. Only these were considered for further compression. A notable contribution is reconstruction error minimization using the extracted features from the residual signal using a deep autoencoder (DAE), achieving a low percentage root-mean-squared difference (PRD). The SQAGC technique was evaluated using public databases like MIMIC-II, BIDMC, and PRRB as well as with real volunteers’ PPG collected in the laboratory environment. The SQA achieved an accuracy of 96.5% to identify good quality PPG segments out of expert annotated 9200 beats. The compression factor (and PRD) with 400 min duration data from Physionet MIMIC-II, BIDMC, PRRB, and volunteers’ data were 15.8 (and 0.31), 15.7 (and 0.21), 17.8 (and 0.33), and 18.2 (and 0.59), respectively, using 12-bit resolution and 125 Hz sampling. A real-time on-device implementation using quad-core ARM Cortex-A53, 1.2 GHz, supported by 1 GB RAM, achieved a latency of 546 ms with 327 kB of memory engagement for a 3 s PPG window. The compression ratio (CR) achieved comparable results, while PRD outperforms the published results using MIMIC-II data set.read more
Citations
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Journal ArticleDOI
Deep convolutional neural network-based signal quality assessment for photoplethysmogram
TL;DR: In this paper , the authors developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database.
Journal ArticleDOI
Real-time mental stress detection technique using neural networks towards a wearable health monitor
TL;DR: In this article , a real-time stress detection technique is presented which utilizes only a photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques.
Journal ArticleDOI
Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration
Laura Gutiérrez-Martín,Elena Romero-Perales,Clara Sainz de Baranda Andújar,Manuel F. Canabal-Benito,Gema Esther Rodríguez-Ramos,R. Toro-Flores,Susana López-Ongil,Celia Lopez-Ongil +7 more
TL;DR: This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality.
Proceedings ArticleDOI
Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment for Physiological Monitoring
TL;DR: This paper presents an ongoing work towards the design of an interval type-II fuzzy-based system for the PPG signal quality assessment, which has great potential for integrating accurate and reliable continuous health monitoring systems into constrained edge devices.
Proceedings ArticleDOI
Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment for Physiological Monitoring
TL;DR: In this paper , an interval type-II fuzzy-based system for photoplethysmography signal quality assessment is presented, which uses a reduced set of features together with a low complexity fuzzy rule base Mamdani inference model, and is based on a nonoverlapping 3-second signal processing window.
References
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Photoplethysmography and its application in clinical physiological measurement.
TL;DR: Photoplethysmography is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue and is often used non-invasively to make measurements at the skin surface.
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Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring
TL;DR: In this article, the authors proposed a signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications.
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Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
Qiao Li,Qiao Li,Gari D. Clifford +2 more
TL;DR: This work introduces dynamic time warping to stretch each beat to match a running template and combines it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped to assess the clinical utility of PPG traces.
Journal ArticleDOI
Optimal Signal Quality Index for Photoplethysmogram Signals.
TL;DR: The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F1 scores of 86.0%, 87.2%, and 79.1%, respectively.
Journal ArticleDOI
Use of Fourier Series Analysis for Motion Artifact Reduction and Data Compression of Photoplethysmographic Signals
TL;DR: Experimental results indicate that the proposed method is insensitive to heart rate variation, introduces negligible error in the processed PPG signals due to the additional processing, preserves all the morphological features of the PPG, provides 35 dB reduction in motion artifacts, and achieves a data compression factor of 12.