Biomedical subjects

M A Jabri

Publications and source records attributed to M A Jabri.

4 recordsLinked to original sources

PubMed1999

A low-complexity intracardiac electrogram compression algorithm.

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patients morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity.

Algorithms↗

PubMed1995

Artificial neural network-based channel selection and loudness mapping.

We present in this paper artificial neural network techniques for implementing loudness mapping and "smart" channel selection for cochlear implant systems. For loudness mapping, a multilayer perceptron (MLP) is trained to perform the mapping for each channel according to threshold and comfort levels. It is shown that good accuracy mapping can be performed by a very simple MLP architecture. For channel selection, we propose a neural network-based method that can make "smart" selection. We describe and report results for the case in which 6 channels are to be selected from 18. The neural network-based selection system is trained on a multispeaker labeled speech database and tested on a database of different speakers and spoken sentences. Compared with methods used by leading cochlear implant systems, our approach produces significantly better results, and it is easy to implement in the speech processor of the cochlear implant system.

Cochlear Implants↗

PubMed1993

Kakadu--a low power analogue neural network classifier.

An analogue neural network VLSI chip designed for low power operation is presented. This chip consists of 84 synapse elements arranged as arrays of size 10 x 6 and 6 x 4 and was fabricated using a standard 1.2 micron double metal single poly CMOS process. The synapses are digitally programmable and static weight storage is provided. The chip has a typical power consumption of tens of microwatts. It has been successfully trained and tested on a range of classification problems including 4-bit parity, character recognition and morphological-based classification of intracardiac electrogram signals.

Algorithms↗

PubMed1992

MATIC--an intracardiac tachycardia classification system.

The use of an additional atrial sensing electrode together with a morphology recognition algorithm provides a significant improvement in classification performance over the current rate based algorithms used in implantable cardioverter defibrillator (ICD) devices. The classification system, called morphology and timing intracardiac classifier (MATIC), follows a classification process similar to that used by cardiologists. Timing between the atrial and ventricular channels is examined using a decision tree and forms the primary criterion for arrhythmia classification. A neural network based morphology classifier is used for cases such as ventricular tachycardia with 1:1 retrograde conduction where timing alone cannot make a reliable decision. MATIC achieves 99.6% correct classification on a database of intracardiac electrogram (ICEG) signals containing 12,483 QRS complexes recorded from 67 patients during electrophysiological studies. Arrhythmias in this database include sinus tachycardia, normal sinus rhythm, normal sinus rhythm with bundle branch block, sinus tachycardia with bundle branch block, atrial fibrillation (AF), various supraventricular tachycardias, ventricular tachycardia, ventricular tachycardia with 1:1 retrograde conduction, and ventricular fibrillation. Within these arrhythmias, there were numerous ventricular ectopic beats, fusion beats, noise, and other artifacts. MATIC addresses the classification problem from start to finish, inputs being raw intracardiac electrogram signals and the outputs being the recommended ICD therapy. Results achieved with MATIC were compared with a classifier used in the Telectronics Guardian ATP 4210, which achieved 75.9% correct classification on the same database. MATIC is simple and efficient, making it suitable for use in a low power implantable device.

Defibrillators, Implantable↗