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Perioperative mortality in coronary artery bypass grafting is usually caused by reduced left ventricular function due to regional myocardial ischemia or infarction. Post-operative graft occlusion is a well-known problem in coronary surgery. A sensitive tool to detect graft occlusion and monitor myocardial function may give the opportunity to revise malfunctioning grafts before departure from the hospital. This paper describes how a new method can detect cardiac ischemia using a 3-axis piezoelectric accelerometer. In three anesthetized pigs, a 3-axis piezoelectric accelerometer was sutured on the lateral free wall of the left ventricle. The left anterior descending (LAD) was occluded for different time periods and the accelerometer data were sampled with a PC. Short-time Fourier transform was calculated based on the accelerometer time series. The results were visualized using a 2D color-coded time–frequency plot. In the area of occlusion, a change to stronger power of higher harmonics was observed. Consequently, a difference value between the instant frequency pattern and a reference frequency pattern showed a rise in absolute value during the occlusion period. The preliminary results indicate that early recognition of regional cardiac ischemia is possible by analyzing accelerometer data acquired from the three animal trials using the prototype 3-axis accelerometer sensor.

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Abstract-Ground cover classification based on a single satellite image can be challenging. The work reported here concerns the use of multitemporal satellite image data in order to alleviate this problem. We consider the problem of vegetation mapping and model the phenological evolution of the vegetation using a Hidden Markov Model (HMM). The different vegetation classes can be in one of a predefined set of states related to their phenological development. The characteristics of a given class are specified by the state transition probabilities as well as the probability of given satellite observations for that class and state. Classification of a specific pixel is thus reduced to selecting the class that has the highest probability of producing a given series of observations for that pixel. Compared to standard classification techniques such as maximum likelihood (ML) classification, the proposed scheme is flexible in that it derives its properties not only from image specific training data, but also from a model of the temporal behavior of the ground cover. It is shown to produce results that compare favorably to those obtained using ML classification on single satellite images, it also generalizes better than this approach.