Unreadable Segment Recognition of Single-Lead ECG Signals Based on XGBoost: Fuision of Shannon Energy Envelope and Empirical Mode Decomposition

The quality of ECG signals is commonly affected by severe noise, especially for the single-lead ECG signals acquired from long-term wearable devices. Recognizing and ignoring these interfered signals can reduce the error rate of automatic ECG analysis system, and in addition, improve the efficiency of cardiologists. Based on XGBoost classifier, we propose an unreadable ECG segment recognition method using features extracted through Shannon Energy Envelope (SEE) and Empirical Mode Decomposition (EMD). An unreadable CarePatchTM ECG patch database is established, containing 8169 readable segments and 6114 unreadable segments with a length of 10 seconds. The XGBoost with 5-fold cross-validation is applied and obtained an accuracy of 99.51+/-0.15%. In conclusion, SSE and EMD features contribute to the unreadable segments recognition and alleviate the misdiagnosis of abnormal rhythms.

Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)

The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.