Showing papers by "Goutam Saha published in 2014"

Classification of emotions induced by music videos and correlation with participants’ rating

Abstract: Emotional experience and preference play a vital role in selection of multimedia content for an individual. Brain electrical activity bears the emotional cues needed for emotion detection, but very modest research has been done to extract those cues. This paper presents a novel machine learning approach using Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) time–frequency features from electroencephalogram (EEG) to detect emotions together with an analysis of brain activity in different emotional states. Firstly, DT-CWPT is used to extract time–frequency emotional features. Then non-redundant and most discriminating emotional features are selected through singular value decomposition (SVD), QR factorization with column pivoting (QRcp) and F-Ratio based feature selection (FS) method. The reduced emotional feature set is used to classify emotion using support vector machine (SVM) and validated by leave-one-out cross-validation scheme. Results confirm the robustness and consistency in classification of emotions from EEG signals and significant correlation between participants’ self assessed ratings with emotional features. It also gives an analysis of activities in brain region during different emotional states.

Detection of lungs status using morphological complexities of respiratory sounds.

Abstract: Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

A Discrete Wavelet Packet Transform Based Approach for Selection of Artifact-Free Phonocardiogram Segment

Abstract: Phonocardiogram (PCG) signals are often corrupted by different types of artifacts which make computer based heart sound signal analysis more challenging. It requires human intervention to select decent quality of signal subsequence that is free from artifacts. In this work, we have proposed a new technique of automatic artifact-free subsequence selection from PCG signal by the application of Discrete Wavelet Packet Transform (DWPT) and quasiperiodicity property of artifact-free heart sound signal. DWPT is used to identify those parts of the signal, where the artifacts are prominent. After this, quasi-periodicity property of PCG signal is used as quality measure to select artifact-free subsequences. The algorithm shows good results when tested on normal and five commonly occurring pathological heart sound signals.