Prof. K. T. Talele

Medical practitioners analyze the electrical activity of the heart in order to detect or predict various disorders by interpreting the information provided by the Electrocardiogram signal of the patient. This project aims at processing and matching of an ECG signal with a database for the detection of abnormal heart conditions based on pattern comparison. The basis for the pattern matching algorithm without feature extraction is an ECG wave from database with ECG signals and patient diagnosis information. The recognition takes place by comparing the wave forms of 3 leads of the examined ECG with the same leads of different ECGs from the database. The similarity of the compared ECG beats is calculated with the cross correlation function. By means of the algorithm, we select ECGs of the database which are the most similar to the examined ECG considering all of the individual leads. Using the available database information, the diagnosis which corresponds to the most similar ECGs of the database is then identified as the diagnosis for the examined ECG. In our project we have used a Texas Instruments TMS320C6713 DSK to generate and process the electrocardiogram signal in digital domain.

The technology of the automatic speech recognition is in full grow, a multitude of algorithms have been developed to improve the performance and robustness of ASR (Automatic Speech Recognition) systems. Automatic Speech recognition systems are increasingly widespread and used in very different acoustic conditions, and by very different speakers. The use of Mel frequency cepstral coefficients (MFCCs) for music information retrieval is one of the standard methods used in ASR systems. This paper describes a method to generate and process the Speech signal in digital domain using Texas Instruments’ TMS320C6713 DSK. Our aim is to develop software to recognize the speech samples from different users so as to restrict access to a predefined set of users. For this purpose, we form a database of different speech samples. The MFCCs for a particular Speech signal is unique for every individual. Therefore every such signal will generate different MFCCs. These are then compared with the previously stored MFCCs of signals to check if any match is found. For real time processing of Speech signal, fast processors like Digital Signal Processors are required.