Photoplethysmography and its application in clinical physiological measurement

A survey on ECG analysis

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

In a world where the industry of mobile applications is continuously expanding and new health care apps and devices are created every day, it is important to take special care of the collection and treatment of users' personal health information. However, the appropriate methods to do this are not usually taken into account by apps designers and insecure applications are released. This paper presents a study of security and privacy in mHealth, focusing on three parts: a study of the existing laws regulating these aspects in the European Union and the United States, a review of the academic literature related to this topic, and a proposal of some recommendations for designers in order to create mobile health applications that satisfy the current security and privacy legislation. This paper will complement other standards and certifications about security and privacy and will suppose a quick guide for apps designers, developers and researchers.

Privacy and Security in Mobile Health Apps: A Review and Recommendations

Wearable health monitoring is an emerging technology for continuous monitoring of vital signs including the electrocardiogram (ECG). This signal is widely adopted to diagnose and assess major health risks and chronic cardiac diseases. This paper focuses on reviewing wearable ECG monitoring systems in the form of wireless, mobile and remote technologies related to older adults. Furthermore, the efficiency, user acceptability, strategies and recommendations on improving current ECG monitoring systems with an overview of the design and modelling are presented. In this paper, over 120 ECG monitoring systems were reviewed and classified into smart wearable, wireless, mobile ECG monitoring systems with related signal processing algorithms. The results of the review suggest that most research in wearable ECG monitoring systems focus on the older adults and this technology has been adopted in aged care facilitates. Moreover, it is shown that how mobile telemedicine systems have evolved and how advances in wearable wireless textile-based systems could ensure better quality of healthcare delivery. The main drawbacks of deployed ECG monitoring systems including imposed limitations on patients, short battery life, lack of user acceptability and medical professional's feedback, and lack of security and privacy of essential data have been also discussed.

A comprehensive survey of wearable and wireless ECG monitoring systems for older adults

Wireless human health monitoring using different techniques has been a popular research area in the last few decades. In this paper, we describe the development of a biomedical sensor node (BSN) fo...

An Intelligent and Power Efficient Biomedical Sensor Node for Wireless Cardiovascular Health Monitoring

Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers’ data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts’ annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ∼0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks

Electrocardiogram (ECG) is an important tool for investigation of cardiac functions. ECG synthesis or modeling can be useful for biomedical applications involving data compressions, signal analysis and testing of medical systems. In this paper, we present a morphological modeling method of single lead ECG by two different approaches, viz., Fourier and Gaussian models. Single lead ECG data was preprocessed to remove unwanted noise and segmented in three zones, P-R, Q-R-S and S-T. The individual segments were then modeled to extract model coefficients. The residual of each segment, computed as difference between original and reconstructed samples were also modeled using Fourier model. The algorithms were validated with lead V2 from normal and Anterior Myocardial Infarction (AMI) data from Physionet. With 25 AMI datasets, the average PRDN and SNR were found to be 9.33 and 20.71 respectively with Gaussian model and 5.43 and 22.16 respectively with Fourier model. The Fourier model showed better reconstruction performance, but less memory efficient compared to the Gaussian model.

Electrocardiogram synthesis using Gaussian and fourier models

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.

On-Board Signal Quality Assessment Guided Compression of Photoplethysmogram for Personal Health Monitoring

Noninvasive monitoring of respiratory activity is an emerging research area in biomedical health monitoring. This article describes a neural network-based model, intelligent Photoplethysmography derived Respiration signal Extraction, and Tracking ( ${i}$ -PRExT). Here, an ensemble empirical mode decomposition (EEMD) is used to select the appropriate intrinsic mode functions (IMFs) through filtering in the respiration band and reconstruct by a linear weighted sum to obtain the photoplethysmography derived respiration (PDR) signal. The weight factors are derived by a multilayer perceptron neural network (MLPNN) fed with respiratory induced amplitude variation (RIAV) features extracted by a deep autoencoder (DAE). The tracking of respiration rate (RR) is done by an adaptive filter-based predictor. ${i}$ -PRExT was tested and validated with BIDMC data set under PhysioNet and 30 volunteers’ data collected under resting condition. The PDRs achieved over 90% correlation and low error (NRMSE~0.2) with reference respiration signal, while RRs have almost 100% correlation even under motion artifact (MA) corrupted photoplethysmography (PPG). The PDR shows improved performance, while RR tracking outperforms the published research on respiration signal extraction based on PPG.

Rajarshi Gupta

Papers

An Intelligent and Power Efficient Biomedical Sensor Node for Wireless Cardiovascular Health Monitoring

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks

Electrocardiogram synthesis using Gaussian and fourier models

On-Board Signal Quality Assessment Guided Compression of Photoplethysmogram for Personal Health Monitoring

i -PRExT: Photoplethysmography Derived Respiration Signal Extraction and Respiratory Rate Tracking Using Neural Networks