Dipti Patil

Clinical Depression Analysis Using Speech Features

Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the speech signal. Some queries are raised that which features are more responsible for depression from speech and which classifiers gives good results. By identifying proper features from speech signal system even one can save the life of a patient. In this paper, a survey of speech signal features which relates for depression analysis is presented. Specially focused on adolescence speech. After surveying it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO). Some classification methods for depression analysis from previous studies are summarized.

Cumulative video analysis based smart framework for detection of depression disorders

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning which can hamper the lives of individuals. The absence of any objective diagnostic aid for depression leads to a range of biases in the diagnosis and ongoing monitoring. This study throws light upon the contribution of gestures and facial points for depression analysis. This paper discusses a novel cumulative video analysis proposed by us based on action units and fictional points for analysis of facial moment. Experimental results are carried out using real world clinical data and interactive sessions with neuro experts. This smart framework developed by us is useful for detection of depression disorders through gesture recognition. The diagnosis is done and appropriate action is taken according to scale of the depression in the patient and the severity of it.

Growing Hierarchical Self-Organizing Map (GHSOM) for Mining Gene Expression Data

This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) reported in the specified writing. Investigating gene expression data is a very difficult problem due to the large amount of genes inspected. Computational methods have proved reliable to make sense of large amounts of data like the data obtained from microarray analysis. In this paper, we present inadequacies of standard algorithms K-Mean and self-organizing Map (SOM) and how GHSOM overcome these.

Concept Adapting Real-Time Data Stream Mining for Health Care Applications

Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Vital signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in health care environment.

Dynamic data mining approach to WMRHM

Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Our approach focused towards handling all kinds of vital signals like ECG, EMG, SpO2 etc. which previous work was not supporting. While predictions the framework considers all parameters like patient history, domain experts rules and continuously monitored real-time signals. Implementation and results of applying clustering algorithms (Graph theoretic, K-means) on patients historical health data for forming the health rule base are discussed here. The framework has been designed to perform the analysis on the instantaneous and stream (continuous) data over a sliding time window which applies dynamic data mining on the live data. The comparative analysis on vital signals made from various clustering algorithms adds extra dimension to global risk alerts and help doctors to diagnose more accurately.