Zhen-Xing Zhang

New algorithm for the depression diagnosis using HRV: A neuro-fuzzy approach

Recent research indicates a significant relationship between the severity of depression and heart rate variability (HRV). This paper presents a neuro-fuzzy approach-based classification algorithm, which distinguishes patients with depression from controls by a neuro-fuzzy network with a weighted fuzzy membership function (NEWFM) using the two time domain and four frequency domain features of HRV. The HRV data were collected from 10 patients with depression and an equal number of healthy controls. Wearing a wireless Holter monitor, each subject underwent a 13-minute multimodal affective contents stimulus, which can induce a variety of emotions. HRV activity was transformed and recorded from periods of 13-minute ECG signals. With a reliable accuracy rate of 95%, the six HRV features were extracted and used as NEWFM input features for depression classification. The standard deviation of the RR intervals (SDNN) and very low frequency (VLF) of HRV were evaluated as good features-from six features-by a non-overlap area distribution measurement method. The two features reflected conspicuous differences between the depression diagnosed and the healthy subjects, which indicates a significant association between depression and the autonomic nervous system. The proposed algorithm will be implemented as a depression monitoring system in a Smartphone application.

Real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias

This paper presents a real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. This detection algorithm focuses on two life-threatening arrhythmias ventricular tachycardia and fibrillation (VT/VF), which are detected through the application of pre-detection processing and main detection processing. In pre-detection processing, applies a statistical method to detect VT/VF. In contrast, a neural fuzzy network is applied to detect VT/VF in main detection processing. The neural fuzzy networks input features are obtained by wavelet transform and several effective extraction methods. This real-time detection algorithm outperform Amanns algorithm, with 92% accuracy and 93% sensitivity. It has been implemented as a cardiac monitoring system in a mobile phone. This system meets heart patients requirements of early detection and out-of-hospital rehabilitation.

Detecting ventricular arrhythmias by NEWFM

The ventricular arrhythmias including ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening heart diseases. This paper presents an approach to detect normal sinus rhythm (NSR) and VF/VT using the neural network with weighted fuzzy membership functions (NEWFM). NEWFM classifies NSR and VF/VT beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using one input features from the Creighton University Ventricular Tachyarrhythmia Data Base (CUDB). In this paper, six input features are obtained from two steps. In the first step, 8s original ECG signal are transformed by Haar wavelet function, and then 256 coefficients of d3 at levels 3 are obtained. In the second step, six input features are obtained by phase space reconstruction (PSR) algorithm using 256 coefficients of d3 at levels 3. The one generalized feature is extracted by the non-overlap area distribution measurement method. The one generalized feature is used for the VF/VT data sets with reliable accuracy and specificity rates of 90.1% and 92.2%, respectively.

Comparison of feature selection methods in ECG signal classification

In past years, the Principal Component Analysis (PCA) has been applied to select features for classification applications. This paper presents a performance comparison between PCA and Non-overlap Area Distribution Measurement (NADM), which is based on a neural fuzzy network. This paper performs an experiment on Normal Sinus Rhythm (NSR) and Ventricular Tachycardia/Fibrillation (VT/VF) classification with the two feature selection methods. The performance result is 89.34% while the number of initial features is projected from six to four by the PCA method. The performance result is 91.02% while the number of initial features is decreased from six to two by NADM. The results clearly show that NADM outperforms PCA by 1.68% with fewer features.

A 2-D Visual Model for Sasang Constitution Classification Based on a Fuzzy Neural Network

The human constitution can be classified into four possible constitutions according to an individual’s temperament and nature: Tae-Yang (太陽), So-Yang (少陽), Tae-Eum (太陰), and So-Eum (少陰). This classification is known as the Sasang constitution. In this study, we classified the four types of Sasang constitutions by measuring twelve sets of meridian energy signals with a Ryodoraku device (良導絡). We then developed a Sasang constitution classification method based on a fuzzy neural network (FNN) and a two-dimensional (2-D) visual model. We obtained meridian energy signals from 35 subjects for the So-Yang, Tae-Eum, and So-Eum constitutions. A FNN was used to obtain defuzzification values for the 2-D visual model, which was then applied to the classification of these three Sasang constitutions. Finally, we achieved a Sasang constitution recognition rate of 89.4 %.

Emotional-speech recognition using the neuro-fuzzy network

Emotion recognition based on a speech signal is one of the intensively studied research topics in the domains of human-computer interaction and affective computing. The presented paper is concerned with emotional-speech recognition based on the neuro-fuzzy network with a weighted fuzzy membership function (NEWFM). NEWFM has a feature selection method and makes fuzzy classifiers. In this paper, NEWFM was utilized for classifying four kinds of emotional-speech signals. This NEWFM classification method achieves as high as 86% overall classification accuracy. Significantly, the NEWFM classifier efficiently detects sadness, with a 97.5% recognition rate.

Discrimination of ventricular arrhythmias using NEWFM

The ventricular arrhythmias including ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening heart diseases. This paper presents a novel method for detecting normal sinus rhythm (NSR), VF, and VT from the MIT/BIH Malignant Ventricular Arrhythmia Database using the neural network with weighted fuzzy membership functions (NEWFM). This paper separates pre-processing into 2 steps. In the first step, ECG beasts are transformed by using Filtering Function [1]. In the second step, transformed ECG beasts produce 240 numbers of probability density curves and 100 points in each probability density curve using the probability density function (PDF) processing. By using three statistical methods, 19 features can be generated from these 100 points of probability density curve, which are the input data of NEWFM. The 15 generalized features from 19 PDF features are selected by non-overlap area measurement method [4]. The BSWFMs of the 15 features trained by NEWFM are shown visually. Since each BSWFM combines multiple weighted fuzzy membership functions into one using bounded sum, the 15 small-sized BSWFMs can realize NSR, VF, and VT detection in mobile environment. The accuracy rates of NSR, VF, and VT is 98.75%, 76.25%, and 63.75%, respectively.

Comparing the Feature Selection Using the Distributed Non-overlap Area Measurement Method with Principal Component Analysis

This paper compares the forecasting performance of the feature extraction using the principal component analysis (PCA) that is one of the oldest and best known techniques in multivariate analysis with the feature selection using the non-overlap area distribution measurement method based on the neural network with weighted fuzzy membership functions (NEWFM). This paper proposes CPPn,m (Current Price Position of day n : a percentage of the difference between the price of day n and the moving average of the past m days from day n-1) as a new technical indicator. In this paper, two and one input features with the best average forecasting performance are selected from the number of approximations and detail coefficients made by Haar wavelet function from CPPn,5 to CPPn-31,5 using the non-overlap area distribution measurement method and PCA, respectively. The performance results of the non-overlap area distribution measurement method and PCA are 60.93% and 56.63%, respectively. The non-overlap area distribution measurement method outperforms PCA by 4.3% for the holdout sets.

Ventricular Fabrication Prediction Approach Based on Cloud-Mobile Healthcare Platform

Sudden Cardiac Death (SCD) is an important risk factor for primary Ventricular Fibrillation (VF). This paper presents a prediction algorithm of VF based on Cloud-Mobile Healthcare platform. This algorithm applies heart rate variability (HRV) features and neural fuzzy network. The neural fuzzy networks input features are obtained by linear and nonlinear features of HRV. The experimental results show that the combination of features can predict VF by the accuracy of 65% for the five minutes intervals, before VF occurrence. It has been implemented in Cloud-Mobile Healthcare Platform. This Cloud-Mobile Healthcare Platform meets heart patients requirements of early detection of outside the hospital.

Emotion Recognition Algorithm Based on Neural Fuzzy Network and the Cloud Technology

Emotion recognition has been a popular topic of human-computer interaction and affective computing. In this paper, 2-D visual emotion recognition model is put forward which takes neural fuzzy network as the theoretical basis. The model is designed for make classification of four types of emotion-related voice signals. A high accuracy of 85% is realized by the model. It is noted that the model can identify sadness emotions effectively. The rate is up to as high as 100%. The algorithm is to be used for a cloud server.