In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

/pdf/the-principles-of-software-qrs-detection-1vgts9f7g6.pdf

The principles of software QRS detection

https://www.repositorio.ufop.br/bitstream/123456789/7011/1/ARTIGO_ECGHeartBeat.pdf

ECG-based heartbeat classification for arrhythmia detection

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Electrocardiograph (ECG) triggered or gated magnetic resonance methods are used in many imaging applications. Therefore, a reliable trigger signal derived from to the R-wave of the ECG is essential, especially in cardiac imaging. However, currently available methods often fail mainly due to the artifacts in the ECG generated by the MR scanner itself, such as the magnetohydrodynamic effect and gradient switching noise. The purpose this study was to characterize the accuracy of selected R-wave detection algorithms in an MR environment, and to develop novel approaches to eliminate imprecise triggering. Vectorcardiograms (VCG) in 12 healthy volunteers exposed to 1.5 T magnetic field were digitized and used as a reference data set including manually corrected onsets of R-waves. To define the magnetohydrodynamic effect, the VCGs were characterized in time, frequency, and spatial domains. The selected real-time R-wave detection algorithms, and a new "target-distance" VCG-based algorithm were applied either to standard surface leads calculated from the recorded VCG or to the VCG directly. The flow related artifact was higher in amplitude than the R-wave in 28% of the investigated VCGs which yielded up to 9-16%false positive detected QRS complexes for traditional algorithms. The "target-distance" R-wave detection algorithm yielded a score of 100% for detection with 0.2% false positives and was superior to all the other selected methods. Thus, the VCG of subjects exposed to a strong magnetic field can be use to separate the magnetohydrodynamic artifact and the actual R-wave, and markedly improves the trigger accuracy in gated magnetic resonance scans. Magn Reson Med 42:361-370, 1999.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291522-2594%28199908%2942%3A2%3C361%3A%3AAID-MRM18%3E3.0.CO%3B2-9

Novel real-time R-wave detection algorithm based on the vectorcardiogram for accurate gated magnetic resonance acquisitions.

Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes

We propose a new speech enhancement method based on the time adaption of wavelet thresholds The time dependence is introduced by approximating the Teager energy of the wavelets coefficients This technique does not require an explicit estimation of the noise level or of the a priori knowledge of the SNR, which is usually needed in most of the popular enhancement methods Performance of the proposed method is evaluated on speech recorded in real conditions and with artificial noise

/pdf/wavelet-speech-enhancement-based-on-the-teager-energy-5113szifsa.pdf

Wavelet speech enhancement based on the Teager energy operator

DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis

We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to wavelet packet thresholding. This technique does not require an explicit estimation of the noise level or of the a priori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MEL-scale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of signal-to-noise ratio (SNR) is reported for time adaptation and time-scale adaptation of the wavelet coefficients thresholds. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models speech recognition experiments are conducted on the AURORA-2 database and show that the proposed method improves the speech recognition rates for low SNRs.

/pdf/wavelet-speech-enhancement-based-on-time-scale-adaptation-475zd3541s.pdf

Mohammed Bahoura

Papers

Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes

Wavelet speech enhancement based on the Teager energy operator

DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis

Wavelet speech enhancement based on time-scale adaptation

An integrated automated system for crackles extraction and classification