Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.

Parkinson's disease is a neurodegenerative condition diagnosed on patients with

clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated

number of patients living with Parkinson's disease around the world is seven

to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor

signs of Parkinson's disease patients. It is an advanced surgical technique that is used

when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.

This work proposes a behavior recognition model for patients with Parkinson's

disease. In particular, an adaptive learning method is proposed to classify behavioral

tasks of Parkinson's disease patients using local field potential and electrocorticography

signals that are collected during DBS implantation surgeries. Unique patterns

exhibited between these signals in a matched feature space would lead to distinction

between motor and language behavioral tasks. Unique features are first extracted

from deep brain signals in the time-frequency space using the matching pursuit decomposition

algorithm. The Dirichlet process Gaussian mixture model uses the extracted

features to cluster the different behavioral signal patterns, without training or

any prior information. The performance of the method is then compared with other

machine learning methods and the advantages of each method is discussed under

different conditions.