Feng Jin

• Doctor of Philosophy
• Toronto Metropolitan University

The QRS complex recorded from the surface electrocardiogram (ECG) arises from electrical activation of the ventricular myocardium through the normal conduction system. The presence of a fragmented QRS (fQRS) complex reflects abnormal electrical activation and has been recently shown to identify patients with heart disease at risk of sudden cardiac...

In this paper, we address the preparation of ecological datasets for data mining. We propose a new adaptive method for automatic dataset construction using Erblet transform, which can be seen as a non-uniform filter bank where the center frequency and the bandwidth of each filter match the ERB (Equivalent Rectangular Bandwidth) scale, followed by d...

Automatic respiratory sound (RS) analysis provides a possible solution for the minimization of inherent subjectivity caused by auscultation via stethoscope, and it allows a reproducible quantification of RS. As one of the crucial initial steps, reliable unsupervised respiratory phase detection plays an important role in automatic RS analysis. In th...

A new method for identification of fish vocalizations based on auditory analysis and support vector machine (SVM) classification is presented. In this method, high resolution features have been extracted from fish vocalization data using the amplitude modulation spectrogram (AMS) of the input signals to facilitate the identification of grunts and g...

This paper presents a novel framework for monitoring fish sounds based on acoustic analysis of noisy big ocean data. The proposed method involves multiresolution acoustic features (MRAF) extraction and RPCA (robust principal component analysis) based feature selection for monitoring of natural fish sounds produced in situ by the plainfin midshipman...

This article addresses the problem of single-channel speech enhancement in the presence of nonstationary noise. A novel-modified NMF-based filter bank approach is proposed for speech enhancement. The method consists of filter bank analysis of the noisy input signal followed by extraction of speech signal based on a modified NMF (MNMF) by learning a...

This article deals with the challenging problem of segmenting narrowly spaced cardiac events (S1 and S2) in noisy phonocardiogram (PCG) signals by using a novel application of NMF based on time-scale approach. A novel energy-based method is proposed for the segmentation of noisy PCG signals in order to detect car- diac events, which could be closel...

Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify respiratory sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency d...

This paper addresses the problem of heart sound (HS) extraction in different types of single-channel respiratory sound (RS) signals by proposing a multiscale mean shift localization approach. First, the incoming respiratory signal (RS) are identified into linear/nonlinear portions by using third-order cumulant. Second, the identified linear and non...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image proc...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new si...

Computerized patient monitoring provides valuable information on clinical disorders in medical practice, and it triggers the need to simplify the extent of resources required to describe large set of complex biomedical signals. In this paper, we present a new signal quantification method based on block-wise similarity measurement between the neighb...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-rel...

Sparse approximation is a novel technique in applications of event detection problems to long-term complex biomedical signals. It involves simplifying the extent of resources required to describe a large set of data sufficiently for classification. In this paper, we propose a multivariate statistical approach using dynamic principal component analy...

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system. Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. This paper proposes a new method for auto­ matic RS classification based on instantaneous frequency (IF) anal­ ysis with the aim to...

In this paper, we attempt to extend single channel source separation techniques to the separation of respiratory sound (RS) and heart sounds (HS). This single channel recording is analyzed and shown to be a convolutive mixture model. After analyzing the reasons for failure of commonly used blind source separation algorithms, we evaluate the efficac...

In this paper, we consider the problem of heart sounds (HS) removal from respiratory sounds (RS), and a novel semi-blind single-channel source extraction algorithm is proposed. The proposed method is able to extract the underlying pure RS from the HS corrupted noisy input signals by incorporating the filter banks and template-based matching using F...

This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located throug...

In this communication, identification of nonlinear portions in tracheal sound (TS) using third-order cumulant has been performed. The tracked nonlinearity has been then analyzed in time-frequency (TF) domain by applying a novel nonlinear analysis method based on optimally weighted Wigner-Ville distributions of the weighted subband signals from a fi...

This paper introduces a novel method to identify inspiratory and expiratory phases from single channel tracheal breath sound (TBS) of different types, by proposing a new anno-tating index name as "mixing index" (MI). An alignment scheme based on phase shift difference information has been firstly introduced to align the consecutive respiratory phas...

This paper addresses the problem of non-invasive respiratory rate (RR) monitoring using single channel tracheal sound (TS) recordings. We have recently developed a robust res-piratory phase segmentation method based on genetic algo-rithm (GA) which works well only for preprocessed clean TS. Therefore, an enhanced respiratory phase monitoring method...

Pulmonary auscultation has been the key method to detect and evaluate respiratory dysfunctions for many years. However, auscultation with a stethoscope is a subjective process that depends on the individual’s own hearing, experience, and ability to differentiate between different sounds (Sovijarvi et al, 2000). Therefore, the computerized method fo...

This paper introduces a novel semi-blind single-channel source extraction algorithm to solve the problem of heart sounds (HS) cancellation from single channel respiratory sounds (RS) recordings. Underlying RS are extracted from those HS contaminated segments in the recorded signal by the proposed algorithm which incorporates shelving filter, filter...

This paper addresses the problem of heart sounds (HS) localization from single channel respiratory sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios c...

In this paper, we propose a robust and automatic wheeze detection method using sample entropy (SampEn) histograms of the filtered narrow band respiratory sound signals. The sound signals are segmented first into their respective inspiration/expiration phases. Time-frequency distribution of each segment is then obtained using Gabor spectrogram. Afte...

In this paper, we address the problem of discriminating normal breath and adventitious, continuous (e.g. wheeze) sounds based on a new adaptive function. The proposed function is based on fast Gabor time-frequency distribution and autoregressive (AR) averaging. Using a time-frequency (TF) representation of the input signal together with a gain proc...

In this paper, a new approach to automatically segment noisy respiratory sound signals is proposed. Segmentation is formulated as an optimization problem and the boundaries of the signal segments are detected using a genetic algorithm (GA). As the estimated number of segments present in a segmenting signal is initially obtained, a multi-population...

This paper proposes a robust segmentation method for differentiating consecutive inspiratory/expiratory episodes of different types of tracheal breath sounds. This has been done by applying minimal Walsh basis functions to transform the original input respiratory sound signals. Decision module is then applied to differentiate transformed signal int...

We suggest a method for automatic identification of respiratory sounds, for example, identifying wheeze from normal breath sounds. Here we apply higher order moments over time and frequency planes. The method is based on the use of efficient fast Gabor spectrogram followed by our recursively measured instantaneous kurtosis and the sample entropy. T...