T. S. Schilder

Bio: T. S. Schilder is an academic researcher. The author has contributed to research in topics: Filter (signal processing) & Band-pass filter. The author has an hindex of 1, co-authored 1 publications receiving 339 citations.

Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps

Abstract: Linear phase filtering is proposed for the removal of baseline wander and power-line frequency components in electrocardiograms. In order to reduce the large number of computations involved in the digital filtering that are necessary, the desired filter spectrum was defined periodically. Making use of the property that the spectrum period is 50 Hz, the spectrum can be realized with a considerably reduced number of impulse response coefficients. This, in combination with the necessary impulse response symmetry, leads to a reduction in the number of multiplications per output sample by a factor of 10. A suitable impulse response is designed with a pass-band ripple of less than 0.5 dB and a high stop-band attenuation. The applicability is demonstrated by applying the filtering to exercise electrocardiograms.

A comparison of the noise sensitivity of nine QRS detection algorithms

Abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >

Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection

Abstract: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy electrocardiogram (ECG), and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm. >

Issues in wearable computing for medical monitoring applications: a case study of a wearable ECG monitoring device

Abstract: In this paper we discuss issues surrounding wearable computers used as intelligent health monitors. Unlike existing health monitors (for example, ECG and EEG holters), that are used mainly for data acquisition, the devices we discuss provide real-time feedback to the patient, either as a warning of impending medical emergency or as a monitoring aid during exercise. These medical applications are to be distinguished from applications of wearable computing for medical personnel, e.g. doctors, nurses, and emergency medical technicians. Medical monitoring applications differ from other wearable applications in their I/O requirements, sensors, reliability, privacy issues, and user interface. The paper describes a prototype wearable ECG monitor based upon a high-performance, low-power digital signal processor and the development environment for its design.

Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm

Abstract: An algorithm for detecting ventricular fibrillation (VF) and ventricular tachycardia (VT) by the method of sequential hypothesis testing is presented. The algorithm first generates a binary sequence by comparing the signal to a threshold. The probability distribution of the time intervals of the binary sequence is obtained, and the sequential hypothesis testing procedure of A.J. Wald and J. Wolfowitz (1948) is employed to discriminate the arrhythmias. Sequential hypothesis testing of 85 cases resulted in identification of (1) 97.64% VF and 97.65% VT episodes after 5 s and (2) 100% identification of both VF and VT after 7 s. The desired false positive and false negative error probabilities can be programmed into the algorithm. An important feature of the sequential method is that extra time for detection can be traded off for improved accuracy, and vice versa. >

Genetic design of optimum linear and nonlinear QRS detectors

Abstract: Describes an approach to the design of optimum QRS detectors. The authors report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described. >