Digital Signal Processing

1301 Submissions

[7] viXra:1301.0120 [pdf] replaced on 2014-01-22 09:47:55

Preliminary Study in Healthy Subjects of Arm Movement Speed

Authors: Mohamed Elgendi, Flavien Picon, Nadia Magnenat-Thalmann, Derek Abbott
Comments: 21 Pages.

Many clinical studies have shown that the arm movement of patients with neurological injury is often slow. In this paper, the speed analysis of arm movement is presented, with the aim of evaluating arm movement automatically using a Kinect camera. The consideration of arm movement appears trivial at rst glance, but in reality it is a very complex neural and biomechanical process that can potentially be used for detecting a neurological disorder. This is a preliminary study, on healthy subjects, which investigates three dierent arm-movement speeds: fast, medium and slow. With a sample size of 27 subjects, our developed algorithm is able to classify the three dierent speed classes (slow, normal, and fast) with overall error of 5.43% for interclass speed classication and 0.49% for intraclass classication. This is the rst step towards enabling future studies that investigate abnormality in arm movement, via use of a Kinect camera.
Category: Digital Signal Processing

[6] viXra:1301.0058 [pdf] replaced on 2014-01-07 22:13:04

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

Authors: Mohamed Elgendi, Bjoern Eskofier, Socrates Dokos, Derek Abbott
Comments: 46 Pages. The paper is published in PLoS ONE and its citation is Elgendi M, Eskofier B, Dokos S, Abbott D (2014) Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems. PLoS ONE 9(1): e84018.

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable batteryoperated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical eciency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.
Category: Digital Signal Processing

[5] viXra:1301.0057 [pdf] replaced on 2013-09-16 17:50:46

Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases

Authors: Mohamed Elgendi
Comments: 37 Pages. The paper is published in PLoS ONE and its citation is: Elgendi M (2013) Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases. PLoS ONE 8(9): e73557

The current state-of-the-art in automatic QRS detection methods show high robustness and almost negligible error rates. In return, the methods are usu- ally based on machine-learning approaches that require sucient computational re- sources. However, simple-fast methods can also achieve high detection rates. There is a need to develop numerically ecient algorithms to accommodate the new trend towards battery-driven ECG devices and to analyze long-term recorded signals in a time-ecient manner. A typical QRS detection method has been reduced to a basic approach consisting of two moving averages that are calibrated by a knowledge base using only two parameters. In contrast to high-accuracy methods, the proposed method can be easily implemented in a digital lter design.
Category: Digital Signal Processing

[4] viXra:1301.0056 [pdf] replaced on 2015-07-21 12:13:10

Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves

Authors: Mohamed Elgendi, Bjoern Eskofier, Derek Abbott
Comments: 33 Pages. The paper is published in Sensors and its citation Elgendi M, Eskofier B, Abbott D (2015) Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves. Sensors 15(7): 17693.

Background: There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies. Methods: Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry). Results: The determination of T wave peaks is performed and the proposed algorithm is evaluated on two~well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats). Conclusions: We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design.
Category: Digital Signal Processing

[3] viXra:1301.0055 [pdf] replaced on 2016-04-13 14:01:29

Can Heart Rate Variability (HRV) be Determined Using Short-Term Photoplethysmograms?

Authors: Mohamed Elgendi, Ian Norton, Matt Brearley, Socrates Dokos, Derek Abbott, Dale Schuurmans
Comments: 23 Pages.

To date, there have been no studies that investigate the independent use of the photoplethysmogram (PPG) signal to determine heart rate variability (HRV). However, researchers have demonstrated that PPG signals offer an alternative way of measuring HRV when electrocardiogram (ECG) and PPG signals are collected simultaneously. Based on these findings, we take the use of PPGs to the next step and investigate a different approach to show the potential independent use of short 20-second PPG signals collected from healthy subjects after exercise in a hot environment to measure HRV. Our hypothesis is that if the PPG–HRV indices are negatively correlated with age, then short PPG signals are appropriate measurements for extracting HRV parameters. The PPGs of 27 healthy male volunteers at rest and after exercise were used to determine the HRV indices: standard deviation of heartbeat interval (SDNN) and the root-mean square of the difference of successive heartbeats (RMSSD). The results indicate that the use of the aa interval, derived from the acceleration of PPG signals, is promising in determining the HRV statistical indices SDNN and RMSSD over 20-second PPG recordings. Moreover, the post-exercise SDNN index shows a negative correlation with age. There tends to be a decrease of the PPG–SDNN index with increasing age, whether at rest or after exercise. This new outcome validates the negative relationship between HRV in general with age, and consequently provides another evidence that short PPG signals have the potential to be used in heart rate analysis without the need to measure lengthy sequences of either ECG or PPG signals.
Category: Digital Signal Processing

[2] viXra:1301.0054 [pdf] replaced on 2014-08-12 12:05:33

Detection of c, d, and e Waves in the Acceleration Photoplethysmogram

Authors: Mohamed Elgendi
Comments: 33 Pages. The paper is published in Computer Methods and Programs in Biomedicine and its citation is: Elgendi M Detection of c, d, and e waves in theacceleration photoplethysmogram. Computer Methods and Programs in Biomedicine. DOI: 10.1016/j.cmpb.2014.08.001

Analyzing the acceleration photoplethysmogram (APG) is becom- ing increasingly important for diagnosis. However, processing an APG signal is challenging, especially if the goal is to detect its small com- ponents (c, d, and e waves). Accurate detection of c, d, and e waves is an important first step for any clinical analysis of APG signals. In this paper, a novel algorithm that can detect c, d, and e waves simul- taneously in APG signals of healthy subjects that have low amplitude waves, contain fast rhythm heart beats, and suffer from non-stationary effects was developed. The performance of the proposed method was tested on 27 records collected during rest, resulting in 97.39% sensitiv- ity and 99.82% positive predictivity.
Category: Digital Signal Processing

[1] viXra:1301.0053 [pdf] replaced on 2014-10-22 17:21:02

Detection of a and B Waves in the Acceleration Photoplethysmogram

Authors: Mohamed Elgendi, Ian Norton, Matt Brearley, Derek Abbott, Dale Schuurmans
Comments: 26 Pages. The paper is published in Biomedical Engineering Online and its citation is: Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D (2014) Detection of a and b waves in the acceleration photoplethysmogram. Biomedical Engineering Online 13: 139.

Background: Analyzing acceleration photoplethysmogram (APG) signals measured after exercise is challenging. In this paper, a novel algorithm that can detect a waves and consequently b waves under these conditions is proposed. Accurate a and b wave detection is an important rst step for the assessment of arterial stiness and other cardiovascular parameters. Methods: Nine algorithms based on xed thresholding are compared, and a new algorithm is introduced to improve the detection rate using a testing set of heat stressed APG signals containing a total of 1,540 heart beats. Results: The new a detection algorithm demonstrates the highest overall detection accuracy|99.78% sensitivity, 100% positive predictivity|over signals that suer from 1) non-stationary eects, 2)irregular heartbeats, and 3) low amplitude waves. In addition, the proposed b detection algorithm achieved an overall sensitivity of 99.78% and a positive predictivity of 99.95%. Conclusions: The proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination.
Category: Digital Signal Processing