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

Photoplethysmography (PPG) is gradually becoming popular tool for cardiovascular and respiratory function monitoring under ambulatory condition. However, these measurements are prone to motion artifact (MA) corruption, and hence, signal quality assessment (SQA) is essential before computerized analysis. The published research on PPG SQA, mostly utilizing supervised learning approaches, suffer from the universality of feature selection against PPG morphology variability. Secondly, beat detection from the MA corrupted is a challenging task and partly limits the success of SQA utilizing beat segmenting approaches. The present research describes an unsupervised learning approach for identification of ‘clean’, ‘partly clean’ and ‘corrupted’ segments in the MA contaminated PPG data. Few entropy features and some signal complexity related features calculated by statistical methods in a 5 s window were fed to a self-organizing map (SOM) for direct quality assessment of PPG data. The number of input node to the SOM was 7 and the output was connected to a square matrix consisting of 25 nodes. The multiclass classification model achieved 94.10%, 89.27%, 92.67% accuracy score for the three classes respectively on 200 min of PPG data collected from 30 healthy and CVD human volunteers under mild to high level of hand movement. The model achieved better result than recently published work utilizing non-segmenting approach based PPG SQA.

Photoplethysmogram Signal Quality Evaluation by Unsupervised Learning Approach

Automatic electrocardiogram analysis using different signal processing technique is one of the prominent areas of research in biomedical engineering. This paper illustrates a simple approach for time plane feature extraction in the QT segment. At first, the R peaks are accurately determined using a simple derivative-based approach and hence heart rate is calculated. Next, the baseline points of all cardiac cycles in the dataset are determined in the TP segment and the baseline modulation in the signal is eliminated by an empirical formula. Finally, the characteristic points Q, Q-offset, S, S-offset, T-onset, T and T-offset are calculated for all cycles using a magnitude and slope threshold based method. Hence, QRS width, ST segment, QT segment and (QT)c are determined. ECG data for 30 second interval from MIT-PTB diagnostic database is used for testing the algorithm.

A Derivative-Based Approach for QT-Segment Feature Extraction in Digitized ECG Record

Remote monitoring of biomedical signals provides an opportunity to extend health care service to a distant patient. In this paper, a short range wireless telecardiology system is described with the objective to transmit electrocardiogram signal for remote end acquisition. The acquired signal was compressed using a combination of modified delta encoding and run length encoding technique and transmitted using a wireless transceiver operating in 2.4 GHz industrial, scientific and medical band to a distance of 400 ft. In the receiving end, error check principle was used to find any data loss before the data is reconstructed for feature extraction. With Physionet data using 8-bit quantization an average compression ratio (CR) of 12.23, percentage root mean squared difference (PRD) of 4.342 and PRD normalized (PRDN) of 9.271 were obtained. With ECG data collected from healthy volunteers, these figures came out to be 14.64, 12.92 and 13.46 respectively. An improvement of performance was observed with 10 bit quantization of ECG data. Computational simplicity of the proposed algorithm provides an opportunity to use a low end microcontroller to implement the compression in standalone hardware.

Wireless Electrocardiogram Transmission in ISM Band: An Approach Towards Telecardiology

Quality controlled compression technique for Photoplethysmogram monitoring applications

Remote health monitoring is a prominent area in modern biomedical research. This involves collection in different biomedical signals from patient using information and communication technology with the objective of remote end assessment of these vital conditions. This paper describes a short range centralized health monitoring system to acquire electrocardiogram (ECG) data using wireless ZigBee communication for computerized analysis. A prototype compact patient data collection system based on ATmega16L microcontroller was developed to collect and compress single lead ECG data for wireless transfer to a centralized station for remote end processing. A state of the art developed software in the central station controlled the patient modules and post acquisition data analysis. Test results with Physionet data and ECG collected from volunteers shown satisfactory result. Average compression achieved using 70 ECG files was 6.93 with average PRD and PRDN of 1.1343 and 8.4645 respectively. Feature extraction results using receiving end ECG data showed an average variance of 0.12%.

Rajarshi Gupta

Papers

Photoplethysmogram Signal Quality Evaluation by Unsupervised Learning Approach

A Derivative-Based Approach for QT-Segment Feature Extraction in Digitized ECG Record

Wireless Electrocardiogram Transmission in ISM Band: An Approach Towards Telecardiology

Quality controlled compression technique for Photoplethysmogram monitoring applications

Short range centralized cardiac health monitoring system based on ZigBee communication