Frequency shifting approach towards textual transcription of heartbeat sounds

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription

Automatic heart diseases detection techniques using musical approaches

In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were stored in a database. Two different approaches which are information retrieval based on n-gram and longest common subsequence are used to retrieve the similarity of a given sample with existing heart diseases in the database. Since the frequency of heart sound is relative to age and physical characteristics of a patient, an important feature of using n-gram in this study is to retrieve diseases without respect to the different heart sounds frequencies. The effects of window sizes for n-gram approach on the accuracy of the information retrieval were tested and a proper window size was extracted. The results of the performed experiments showed that window size of 5 notes revealed a high performance in comparison with other window sizes. Hence, the proposed technique can detect and recognize a heart disease with a reliable accuracy. Average of precision values for around 85% in information retrieval and 55% in longest common subsequence technique were obtained for the retrieval of heart sound categories. Moreover, the results of string matching technique demonstrated that threshold level of 65% could appropriately detect heart disease

Heartbeat disease diagnosis using text-based approaches

Heart sound signals are the important asset for heart examination in primary healthcare centers to aid significantly in the diagnosis of heart diseases. Interpretation of heart sounds is a problematic and difficult skill that requires cardiology specialists. The diagnosis of heart disease from heart sound can differ between cardiologists and would require more detailed and expensive tests. However, heart disease diagnosis by heartbeat is preferable and still widely used as the first step to diagnosis. Computer aided auscultation has emerged as a costeffective technique to analyze and interpret the heart sounds. Digital heart sound recordings with background noise, similarity among heart diseases, recording environment conditions, auscultation body points makes detection of heart diseases complicated. There are several methods for automated detection and classification of heart diseases and heart sound analysis that have been proposed. Some of them used Artificial Neural Network method for detection and classification of heart sounds. Another technique that it used for diagnosis the heart problem is Hidden Markov Model (HMM) that they suggest HMM for segmentation of heart sound recorded for clinical and classification purpose. However, to the best knowledge of the researcher, no prior study has encoded heart sound to text string. In this study, we propose a feasible technique for developing a heartbeat sound retrieval system using text encoding techniques which is useful towards automated heart disease detection. The audio format of heart sound recordings are preprocessed and transcribed into the MIDI format. The MIDI files are then encoded to text strings using the pitch and duration information based on n-gram, these text strings then form musical words. These text strings are then indexed and tested for retrieval using both database and Information Retrieval (IR) systems. The Longest Common Subsequence (LCS) matching algorithm was used for identifying similarities from the database. With IR, full text indexing of the recordings was used and retrieved using known item searches from a search engine. The feasibility of these text encoding techniques were shown from retrieval experiments with around 100 digital heart sound recordings. Overall, experimental results performed clearly showed the feasibility of using proposed text encoding techniques for diagnosing heart problems. Thus, it can be said that the results presented for heart sound retrieval system are very promising for queries in Normal and Abnormal heart sound categories

Frequency shifting approach towards textual transcription of heartbeat sounds

Abstract Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription.</p