The analysis of the ECG can bene t from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classi cation tasks of a possible equipment integrating the most common features of the ECG analysis: arrhythmia, myocardial ischemia, chronic alterations. Several ANN architectures are implemented, tested, and compared with competing alternatives. Approach, structure, and learning algorithm of ANN were designed according to the features of each particular classi cation task. The trade{o between the time consuming training of ANNs and their performances is also explored. Data pre-and post-processing e orts on the system performance were critically tested. These e orts' crucial role on the reduction of the input space dimensions, on a more signi cant description of the input features, and on improving new or ambiguous event processing has been also documented. Finally, the algorithm assessment was done on data coming from all the currently available ECG databases.

With the aim of better understanding the dynamic changes in sympatho-vagal tone occurring during the night, human heart rate variability (HRV) during the various sleep stages was evaluated by means of autoregressive spectral analysis. Each recording consisted of an electroencephalogram, an electrooculogram, and electromyogram, and electrocardiogram, and a spirometry trace. All of the data were sampled and stored in digital form. Sleep was analysed visually, but HRV was analysed off-line by means of original software using Burg's algorithm to calculate the LF/HF ratio (LF: 0.04-0.12 Hz; HF: 0.15-0.35 Hz) for each sleep stage. Seven healthy subjects (four males; mean age 35 years) were enrolled in the study. Our findings show a progressive and significant reduction in the LF/HF ratio through sleep stages S1-S4, as a result of an increase in the HF component; this indicates the prevalence of parasympathetic activity during slow-wave sleep. During wakefulness, S1 and REM, the LF/HF values were similar and close to 1.

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