Computerized classification of corpus cavernosum electromyogram signals bythe use of discriminant analysis and artificial neural networks to supportdiagnosis of erectile dysfunction

Corpus cavernosum electromyogram (CC-EMG) provides diagnostic information o n cavernous autonomic innervation and a measure of the degree to which the cavernous smooth muscle cells are intact. The complicated CC-EMG is evaluat ed and used in the diagnosis of patients suffering from erectile dysfunctio n. The evaluation procedure has been simplified by applying digital signal processing techniques. Since mathematically-based interpretations require q uantitative data, spectral analysis was performed. The derived biosignals w ere analyzed by fast Fourier transform (FFT). Besides various other spectra l parameters, specific frequency bands were determined in the power spectru m using factor analysis. The parameters were used for the computerized clas sification of normal and pathological CC-EMG data and the classification wa s performed using two independent methods: discriminant analysis (DA) and a rtificial neural networks (ANN). A medical expert analyzed a total of 200 C C-EMG recordings from patients with and without erectile dysfunction and se parated these into normal (136) and pathological (64) cases. Although each independent method had already resulted in a relatively high number of corr ect classifications, the classification success rate could be slightly impr oved by using a combination of both classification methods. A total of 72.7 9% and 77.94% were successfully classified using DA and ANN, respectively. The combination of both methods increased the classification success to 80. 15%. The results of this study enabled impartial evaluation of the CC-EMC s ignals for clinical diagnostic purposes of erectile dysfunction. This metho d provided an objective and easy way to analyze the CC-EMG. Furthermore, th is results in patient diagnosis becoming an easier task for less experience d doctors, since little knowledge of the raw signal is needed.