The noise sensitivities for nine different QRS detection algorithms were measured for a normal, single-channel lead II, synthesized ECG corrupted with five different types of synthesized noise. The noise types were electromyographic interference, 60 Hz powerline 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.

Center WITNESSING THE SWIFT ADVANCES in the electronic means of seeing and hearing, scientists and engineers scent a market for systems mimicking the human nose. Already commercial systems from several companies are targeting applications, present and potential, that range from quality assurance of food and drugs to medical diagnosis, environmental monitoring, safety and security, and military use. The harnessing of electronics to measure odor is greatly to be desired. Human panels backed by gas chromatography and mass spectrometry (GC/MS) are helpful in quantifying smells, but they are time-consuming, expensive, and seldom performed in real time in the field. So it is important that these traditional methods give way to a speedier procedure using an electronic nose composed of gas sensors. To be sure, gas sensors have been around for many years. But today's electronic nose technology goes several steps farther. Arrays of sensors that respond to a wide range of compounds are used, as well as advanced pattern recognition and artificial intelligence techniques, which enable users to readily extract relevant and reliable information. So-called electronic noses-systems that detect and identify odors and vapors, typically by linking chemical sensing devices with signalprocessing and pattern-recognition subsystems-go for US $20 000 to $100 000 in Europe, the United States, and Japan, predominantly for laboratory use. Advances in the technology have been made ever since the early 1980s when researchers at the University of Warwick in Coventry, England, developed sensor arrays for odor detection. Focused primarily on the sensor aspect of the problem, the initial research explored the use of metal oxide devices. Later work at Warwick University explored the use of conducting polymers. In both, sensing is based on conductivity changes. Those early efforts have spawned several commercial enterprises. In August 1991, the pioneers organized an advanced research workshop in Reykjavik, Iceland, sponsored by the North Atlantic Treaty Organization. The workshop accelerated interest in the field, and by now there are many groups around the world working on electronic nose technology. The Warwick pioneers envisioned an actual electronic equivalent of the mammalian olfactory system and dubbed their primitive analogs of it the electronic nose. So even though the electronic system resembles its biological counterpart none too closely, the "electronic nose"-or E-nose-label has been widely adopted around the world. The biological nose To attempt to mimic the human apparatus, researchers have identified distinct steps that characterize the way humans smell [see "Design for smelling" by Wolfgang Göpel and Tilo Weiss]. It all begins with sniffing, which moves air samples that contain molecules of odors past curved bony structures called turbinates. The turbinates create turbulent airflow patterns that Receptor: a molecular structure on the surface of a nerve cell to which specific compounds can bind; also a sensory nerve terminal that resp...

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The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace.

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