SUMMARYThe aim of this study is to describe a new false-alarm probability (FAP) bounded unified framework for segmentation of the phonocardiogram (PCG) signal sounds registered by an electronic stethoscope board. To meet this end, first the original PCG signal is pre-processed by application of an appropriate bandpass finite-duration impulse response (FIR) filter and then by implementation of à trous discrete wavelet transform (DWT) to the filtered signal for extracting several dyadic scales. Then, after choosing a proper scale, a fixed sample size sliding window is moved on the selected scale and in each slide, six feature vectors namely summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. Next, using an adaptive smoothing filter (ASF), the obtained metric is modulated and freed from the fast fluctuations occurring in the vicinity of events onset and offset locations which consequently results in enhancement of edge detection accuracy. Later, histogram parameters of the filtered DS metric are used for the regulation of the -level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. To assess the performance quality of the proposed PCG segmentation algorithm, the method was applied to all 85 records of Nursing Student Heart Sounds database including stenosis, insufficiency, regurgitation, gallop, septal defect, sound split, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. The method was also applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as a result, no false positive or false negative errors were detected. High robustness against measurement noises of the electronic stethoscopes, acceptable detection-segmentation accuracy of PCG events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameter dependency on the acquisition sampling frequency can be mentioned as important merits and capabilities of the proposed PCAGI-based PCG events detection-segmentation algorithm.
The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.
The aim of this study is to develop and describe a new ambulatory Holter electrocardiogram (ECG) events detection-delineation algorithm via segmentation of an information-optimized decision statistic. After implementation of appropriate pre-processing, a uniform length sliding window is applied to the pre-processed trend and in each slide, some geometrical features of the excerpted segment are calculated to construct a newly proposed Discriminant Analyzed Geometric Index (DAGI), by application of a nonlinear orthonormal projection. Then the α-level Neyman-Pearson classifier is implemented to detect and delineate QRS complexes. The presented method was applied to several databases and the average values of sensitivity and positive predictivity, Se = 99.96% and P+ = 99.96%, were obtained for the detection of QRS complexes, with an average maximum delineation error of 5.7 ms, 3.8 ms and 6.1 ms for P-wave, QRS complex and T-wave, respectively. Also the method was applied to DAY general hospital high resolution holter data (more than 1500,000 beats, including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC, and Premature Atrial Complex-PAC) and average values of Se = 99.98% and P+ = 99.97% were obtained for QRS detection. High accuracy in a widespread SNR, high robustness and processing speed (146,000 samples/s) are important merits of the proposed algorithm.
The aim of this study is to develop and describe a new ambulatory holter electrocardiogram (ECG) events detection-delineation algorithm with the major focus on the bounded false-alarm probability (FAP) segmentation of an information-optimized decision statistic. After implementation of appropriate preprocessing methods to the discrete wavelet transform (DWT) of the original ECG data, a uniform length sliding window is applied to the obtained signal and in each slid, six feature vectors namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated to construct a newly proposed principal components analyzed geometric index (PCAGI) by application of a linear orthonormal projection. In the next step, the α-level Neyman-Pearson classifier (which is a FAP controlled tester) is implemented to detect and delineate QRS complexes. The presented method was applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7, 3.8 and 6.1 m for P-wave, QRS complex and T-wave, respectively. Also, the proposed method was applied to DAY general hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC and Premature Atrial Complex-PAC) and average values of Se = 99.98% and P+ = 99.97% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 155,000 samples/s can be mentioned as important merits and capabilities of the proposed algorithm.
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