Effect of a Romantic Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of ≥80%.

Design and Development of Capacitance Sensor Array for Analyzing the Non-Homogeneity in Biological Materials

Soft tissues are non-homogeneous deformable structures having varied structural arrangements, constituents, and composition. This chapter explains the design of a capacitance sensor array for analyzing and imaging the non-homogeneity in biological materials. Further, tissue mimicking phantoms are developed using Agar-Agar and Polyacrylamide gels for testing the developed sensor. Also, the sensor employs an unsupervised learning algorithm for automated analysis of non-homogeneity. The reconstructed capacitance image can also be sensitive to topographical and morphological variations in the sample. The proposed method is further validated using a fiberoptic-based laser imaging system and the Jaccard index. In this chapter, the design of the sensor array for smart analysis of non-homogeneity along with significant results are presented in detail.

Spiritual Intelligence and Organizational Commitment

Denoting individual life along organizational commitment (OC) is one of the strategical programs in an organization. Although there are many researches about considering the meaning of organizational commitment and its relationship with other elements, but spiritual intelligence is an effective element of individual and behavior in organizations. The goal of this chapter is the investigation of the relationship between organizational spiritual intelligence (SI) and organizational commitment (OC) in Iranians' social security organization (ISSO). Base on this factor, during reviewing theatrical literature and relevant variable of scaling, the outline of research is made. The strategy of this research is a dependent kind of surveying tool for collecting data standard questionnaire about SI. The dual aspects of spiritual intelligence, self-knowledge and theology, correlated with commitment organization. Also, the main hypothesis of this study is to examine the relationship between organizational commitment and spiritual intelligence.

Use of Orange Data Mining Toolbox for Data Analysis in Clinical Decision Making

Diabetes is a life-long illness which occurs as a result of lack of insulin hormone or ineffectiveness of insulin hormone. Blood sugar, fructosamine, and hemoglobin A1c (HbA1c) values are widely used for diagnosis of this disease. Although the role of insulin in diagnosing diabetes is great, the HbA1c value is more accurate. This is because HbA1c value gives information about the past two or three months of blood sugar in the treatment of diabetes. This study aims to estimate the HbA1c value with high accuracy. Follow-up data of diabetic patients were used as data. The Orange data mining software is used because it is easy to use in the modeling phase and contains many methods. In this context, the chapter aims to develop an effective prediction model by using a large number of feature selection and classification methods. The results show that the proposed model successfully predicts the HbA1c parameter. In addition, determination of the parameters that are effective in the diagnosis of diabetes has been carried out with the feature selection methods.

Development of an Automated Decision Support System for Diagnosis of Digestive Disorders Using Electrogastrograms

The disorders of the digestive tract lead to various problems such as bleeding, bloating, nausea, etc. In order to diagnose various digestive abnormalities, the electrogastrograms (EGG) can serve as an efficient tool. In an EGG, several electrodes are placed onto the abdomen over the stomach and the electrical signals originating from the stomach muscles are recorded. By analyzing these electrical patterns, the abnormalities in digestive system can be analyzed. This chapter describes the developed system for measuring EGG signals along with the decision support system developed for automated classification of digestive disorders. The normal and abnormal EGG signals were acquired at Balaji Medical Hospital, Chennai. Further, the features were extracted using descriptive statistics and empirical mode decomposition (EMD) algorithm. Finally, an automated classification system was developed using k-means algorithm. This chapter explains the recording of electrogastrograms and a method for classification of normal and abnormal EGG signals.

A Medical Decision Support System Based on Ensemble of Complex-Valued Radial Basis Function Networks

The use of machine learning techniques for medical diagnosis has become increasingly common in recent years because, most importantly, the computer-aided diagnostic systems developed for supporting the experts have provided effective results. The authors aim in this chapter to improve the performance of classification in computer-aided medical diagnosis. Within the scope of the study, experiments have been performed on three different datasets, which include heart disease, hepatitis, and BUPA liver disorders datasets. First, all features obtained from these datasets were converted into complex-valued number format using phase encoding method. After complex-valued feature set was obtained, these features were then classified by an ensemble of complex-valued radial basis function (ECVRBF) method. In order to test the performance and the effectiveness of the medical diagnostic system, ROC analysis, classification accuracy, specificity, sensitivity, kappa statistic value, and f-measure were used. Experimental results show that the developed system gives better results compared to other methods described in the literature. The proposed method can then serve as a useful decision support system for medical diagnosis.

A Novel Kernel-Based Fuzzy Clustering Approach to Identify Masses in Mammograms

Detection of breast cancer in form of masses at initial stage becomes difficult because of obscured nature of mammograms by surrounding tissues. This poor visibility of masses addresses to the necessity of accurate contrast enhancement method. This study introduces a novel kernel-based fuzzy clustering approach to enhance the contrast of masses. Novelty of the proposed technique is incorporation of two important features of mammograms that convey the properties of masses. Local entropy and intensity mean of each kernel position play the key role to enhance masses. A kernel is moved across the mammograms to collect all possible values of those features, and hence, they are exploited as the input data of fuzzy clustering technique. Performance of the proposed approach is compared with two other conventional contrast enhancement techniques. Both subjective and objective evaluations of the proposed technique show an evidence of improvement in compare to other methods. Moreover, unwanted enhancement of obscured tissues is also suppressed in the proposed approach.

Wavelet Packet Analysis of ECG signals to Understand the Effect of a Motivating Song on Heart of Indian Male Volunteers

This chapter investigates the effect of a motivational song (acting as a stimulus) on the electrical activity of the heart using wavelet packet analysis of electrocardiogram (ECG) signals. ECG signals were acquired from 18 healthy male volunteers during the pre- and the post-stimulus conditions. Wavelet packet decomposition of the ECG signals was performed up to level 3 using db04 wavelet, which resulted in the formation of 8 wavelet packet coefficients. Linear (t-test) and nonlinear (classification and regression tree [CART], boosted tree [BT], and random forest [RF]) methods were used to identify the statistically significant parameters. The statistically significant parameters were used as categorical inputs for multilayer perceptron (MLP)-based artificial neural network (ANN) classification of the ECG signals. A classification efficiency of ≥ 80% was obtained, suggesting an alteration in the cardiac electrophysiology of the volunteers caused by the music stimulus.