Goutam Saha

Bio: Goutam Saha is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Speaker recognition & Phonocardiogram. The author has an hindex of 24, co-authored 73 publications receiving 1996 citations. Previous affiliations of Goutam Saha include Indian Institutes of Technology.

Investigating neuromagnetic brain responses against chromatic flickering stimuli by wavelet entropies

Abstract: BACKGROUND: Photosensitive epilepsy is a type of reflexive epilepsy triggered by various visual stimuli including colourful ones. Despite the ubiquitous presence of colorful displays, brain responses against different colour combinations are not properly studied. METHODOLOGY/PRINCIPAL FINDINGS: Here, we studied the photosensitivity of the human brain against three types of chromatic flickering stimuli by recording neuromagnetic brain responses (magnetoencephalogram, MEG) from nine adult controls, an unmedicated patient, a medicated patient, and two controls age-matched with patients. Dynamical complexities of MEG signals were investigated by a family of wavelet entropies. Wavelet entropy is a newly proposed measure to characterize large scale brain responses, which quantifies the degree of order/disorder associated with a multi-frequency signal response. In particular, we found that as compared to the unmedicated patient, controls showed significantly larger wavelet entropy values. We also found that Renyi entropy is the most powerful feature for the participant classification. Finally, we also demonstrated the effect of combinational chromatic sensitivity on the underlying order/disorder in MEG signals. CONCLUSIONS/SIGNIFICANCE: Our results suggest that when perturbed by potentially epileptic-triggering stimulus, healthy human brain manages to maintain a non-deterministic, possibly nonlinear state, with high degree of disorder, but an epileptic brain represents a highly ordered state which making it prone to hyper-excitation. Further, certain colour combination was found to be more threatening than other combinations.

Smartphone-Based Point-of-Care System Using Continuous-Wave Portable Doppler

Abstract: Point-of-care Ultrasound (PoCUS) is a safe, repeatable, and inexpensive bedside diagnostic tool. Over the years, PoCUS services are adopted in resource-limited settings for faster and useful outcomes. For a cost-effective and power-efficient solution, a smartphone-based portable continuous-wave Doppler ultrasound (US) system has been developed for diagnosing peripheral arterial diseases based on the hemodynamic feature values. The proposed system includes the analog front end (AFE), signal processing and display unit (SPDU), and smartphone application. The AFE acquires blood flow signal from the brachial artery using an 8-MHz pencil probe, extracts the Doppler shift frequency, and transfers to the SPDU through 12-bit analog-to-digital converter. To provide an area and power-efficient solution, SPDU is embedded in a field-programmable gate array (FPGA)-based single chip. A COordinate Rotation DIgital Computer (CORDIC)-based custom-designed 512-point fast Fourier transform is implemented in that FPGA for displaying the blood flow spectrogram in real time. For back-end processing, the smartphone application receives a spectrogram through Bluetooth, removes noise, extracts hemodynamic features, and diagnoses using a machine learning framework. The device has been examined on 18 volunteers (normal: 17 and abnormal: 1), while the accuracy is found to be 94% in the pretrained support vector machine classifier. For validation, the spectrogram of the normal and abnormal subjects and parameter values are compared with the commercial device. Overall, the handheld device is minimally trained operator-dependent and consumes < 4 W of power for real-time processing. Such smartphone-based feature extraction and automated diagnosis can facilitate the point-of-care system and provide a baseline for early assessment.

On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and C/sub p/ statistic

Abstract: The problem of modeling complex processes with a large number of inputs is addressed. A new method is proposed for the optimization of the models in minimum C/sub p/ statistic sense using QR with a modified scheme of column pivoting (m-QRcp) factorization. Two different classes of multilayer nonlinear modeling problems are explored: 1) in the first class of models, each layer comprises multiple linearly parameterized submodels or cells; the individual cells are optimally modeled using QR factorization, and m-QRcp factorization ensures optimal selection of variables across the layers. 2) The nonhomogeneous feed-forward neural network is chosen as the second class of models, where the network architecture and structure are optimized in terms of best set of hidden links (and nodes) using m-QPcp factorization. In both the cases, the optimization is shown to be direct and conclusive. The proposed is a generic approach to the optimal modeling of complex multilayered architectures, which leads to computationally fast and numerically robust parsimonious designs, free from collinearity problems. The method is largely free from heuristics and is amenable to automated modeling.

Improved Speech-Signal Based Frequency Warping Scale for Cepstral Feature in Robust Speaker Verification System

Abstract: Development of automatic speaker verification system (ASV) for real-world applications remains a major challenge. In this paper, we propose an improved speech-signal-based frequency warping scale to extract cepstral features from the speech signal for ASV application. The proposed scale is a modified version of the speech-signal-based scale, successfully used in speech recognition application, an allied domain. It uses spectral entropy weighted power spectral density to extract speaker specific attributes. This is complementary to fixed scale based mel frequency cepstral coefficient (MFCC) for different emphasis given to spectral regions. The work uses fusion based approach to exploit the complementarity of static MFCC and proposed feature. The performances of the ASV system that uses MFCC and the proposed technique are evaluated in clean and various noisy conditions on publicly available NIST SRE databases. Noise database (NOISEX-92) is used to simulate the noisy environment. The ASV system developed from the proposed feature extraction method shows slightly improved performance than baseline MFCC and SFCC (speech-signal-based frequency cepstral coefficient) based techniques in clean condition and up to 38.15% and 17.15%, respectively in noisy conditions. The fusion-based approach further improves the performance of ASV system with up to 53.85% and 36.22% relative improvement over baseline MFCC and SFCC based feature extraction methods, respectively.

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

Abstract: Background and objective Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. Methods We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. Results The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. Conclusions The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Procedures for numerical analysis of circadian rhythms.

Abstract: This article reviews various procedures used in the analysis of circadian rhythms at the populational, organismal, cellular and molecular levels. The procedures range from visual inspection of time plots and actograms to several mathematical methods of time series analysis. Computational steps are described in some detail, and additional bibliographic resources and computer programs are listed.