Aniruddha J. Joshi

Nadi Tarangini: A Pulse Based Diagnostic System

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

Arterial Pulse System: Modern Methods For Traditional Indian Medicine

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture - to our knowledge for the first time - the self- similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures.

Arterial Pulse Rate Variability analysis for diagnoses

Heart rate variability (HRV) provides an estimate of sympathetic and parasympathetic influences on the heart rate. Although HRV has been extensively studied, sustained clinical use is still outstanding. The noninvasive, convenient, and inexpensive arterial pulse originate from heartbeats, but has not been studied in a systematic fashion except in rudimentary ways. In this paper, we present pulse rate variability (PRV) as an alternative to HRV. We give evidence for the detection of disorders in patients using PRV, paving the way for future clinical use.

Arrhythmia classification using local hölder exponents and support vector machine

We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

Multifractality in arterial pulse

Extensive research has been done to show that heartbeats are composed of the interaction of many physiological components operating on different time scales, with nonlinear and self-regulating nature. The more direct, and easily accessible manifestation of the heartbeat is the pulse; however, it has not been studied anywhere near as extensively. In this paper, we establish the relevance of the multi-fractal formalism for the arterial pulse, which has long been used as a fundamental tool for diagnosis in the Traditional Indian Medicine, (Ayurveda). The finding of power-law correlations through detrended fluctuation analysis indicates presence of scale-invariant, fractal structures in the pulse. These fractal structures are then further established by self-affine cascades of beat-to-beat fluctuations revealed by wavelet decomposition at different time scales. Finally, we investigate how these pulse dynamics change with age, and disorder. The analytic tools we discuss may be used on a wide range of physiological signals.

Hybrid Support Vector Machine for imbalanced data in multiclass arrhythmia classification

Automatically classifying ECG recordings for arrhythmia is difficult since even normal ECG signals exhibit irregularities, and learning algorithms suffer from class imbalance. We propose a hybrid SVM to combat class imbalance rampant in biomedical signals. Consequently, we significantly reduce the number patients falsely classified as normal. The Hybrid SVM is suitable for a variety of multiclass problems; here, we used the MIT-BIH Arrhythmia database, and the position and magnitude of local singularities as features. We enhance relevant singularity-driven Holder features proposed earlier; while the use of these features results in higher accuracy, using the Hybrid SVM shows even more gains.

Hybrid SVM for Multiclass Arrhythmia Classification

Automatically classifying ECG recordings for Malignant Ventricular Arrhythmia is fraught with several difficulties. Even normal ECG signals exhibit only quasi-periodic nature, and contain various irregularities. The key to more accurate detection is the use of position, and amount of local singularities in the signals.In this paper, we propose a Holder-SVM detection algorithm using a novel hybrid arrangement of binary and multiclass SVMs designed to take care of class imbalance rampant in biomedical signals. As a result, we significantly reduce the number of false negatives – patients falsely classified as normal. We used the MIT-BIH Arrhythmia database for even different arrhythmias. We compare our hybrid SVM with a suitable conventional SVM, and show better results.We also use the new arrangement for features proposed earlier, and demonstrate the gain in accuracy. Our concept of hybrid SVM is applicable to a wide variety of multiclass classification problems.

A novel PSWT-DTW approach for analyzing pseudo-periodic signals

Pseudo-periodic signals are rampant in biomedical applications but are difficult to analyze. One approach is to compute time domain parameters of each individual cycle in the pseudo-periodic signal. The classic approach requires repeated computation in each cycle, which tends to be either error prone, computationally burdensome, or requires manual effort. We provide a novel combination of the pitch synchronous wavelet transform which when combined with dynamic time warping results in effective quantification of cycles in the pseudo-periodic signal. We demonstrate our application of this method in studying the arterial pulse. The results show that our approach is feasible and effective, and confirms further scope in other applications.