Korea University · Neuroscience
Professor Sunil Kumar Prabhakar's research lab specializes in computational neuroscience and biomedical data analytics, focusing on the development of advanced machine learning and signal processing techniques for early diagnosis and classification of neurological and oncological disorders. The lab primarily investigates electroencephalography (EEG), photoplethysmography (PPG), and gene expression microarray data to identify biomarkers and classify diseases such as schizophrenia, prostate cancer, and ovarian cancer. A key research direction involves optimizing feature selection and classification pipelines using wavelet transforms and hybrid optimization techniques to handle high-dimensional, noisy biological data.
Figures are computed from collected data and may differ slightly.
One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizop
To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication his
One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area
Prostate Cancer is a cancer that occurs in the prostate- a small walnut shaped gland in men. This gland helps in the production of seminal fluid which is used to nourish and transport the sperm. One of the most common types of cancer in men is prostate cancer. A microarray dataset contains the microarray gene expression information. On a genome wide scale, gene expression profiles make it easy to analyze the patterns between genes and cancers, however the analysis of gene expression data is very
One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A P
Ovarian Cancer is a type of cancer that begins in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which has the ability to spread to other regions of the body. A highly useful diagnostic and prognostic data for ovarian cancer research is provided by the microarray data. Typically, genes with tens of thousands of dimension are present in the microarray data of ovarian cancer. There is a systematic methodology required to analyze this data and so it is important t
The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recor
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. <i>Goal</i>: In this paper, two versatile deep learning methods are proposed for the efficient classification of ep
A plethora of disorders are found in human oral mucosa. A variety and huge number of lesions and diseases in human oral mucosa have been clinically identified and classified. Most lesions have the possibility to develop into oral cancer. The initial diagnosis of oral cancer is to inspect the ocular regions carefully and register the oral cavity of the patient as true-color digital images. The decision about the further treatment of the oral cancer patient is predominantly depending on the appear
This paper aims to compare the performance of code-converter as a feature extraction technique followed by various distance measures such as City Block Distance (CBD) measure and Euclidean Distance (ED) measure for the perfect classification of epilepsy risk levels from Electroencephalography (EEG) Signals. From the extracted parameters such as sharp and spike waves, energy, peaks, duration, variance, events and covariance from the EEG Signals of an epileptic patient, the risk level of epilepsy
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