The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.
ObjectiveTo compare and analyze the electroencephalographic (EEG) characteristics of infants with infantile epileptic spasms syndrome (IESS) and healthy infants during sleep using power spectral density (PSD) analysis. MethodsInfants aged 5 to 9 months with IESS were included, along with an equal number of age-matched healthy controls. EEG signals during sleep were recorded using the Nihon Kohden EEG-1200C system. The energy distribution in the theta (θ), alpha (α), sigma (σ), and beta (β) frequency bands, as well as the morphology and values of PSD within the 4 ~ 30 Hz range, were analyzed. Additionally, spectral entropy (SpEn) was calculated to evaluate signal complexity. Results A total of 10 IESS patients and 10 healthy infants were included. There were no significant differences in gender or age between the two groups (P=0.64, P=0.88). In both groups, PSD values showed a linear decreasing trend with increasing frequency. However, the IESS group showed notable differences in PSD morphology, amplitude, and energy distribution compared to controls. These included the absence of a σ-band peak, greater PSD dispersion across electrodes, significant alterations in energy distribution across θ, α, σ, and β bands, and significantly higher PSD values in the 4 ~ 30 Hz range (P<0.000 1). SpEn analysis revealed significantly elevated spectral entropy across the sigma band in the IESS group, indicating a lack of dominant frequencies, increased complexity, reduced rhythmicity, and enhanced disorder. In contrast, healthy controls exhibited elevated SpEn in the alpha band, reflecting the physiological reduction or disappearance of dominant alpha rhythms during sleep. Conclusion Infants with IESS demonstrate distinct EEG characteristics in both PSD and SpEn analyses compared to healthy infants. These quantitative spectral features reflect the underlying abnormalities of EEG in IESS and provide objective insights that complement conventional visual assessment, offering a novel perspective for early diagnosis and therapeutic monitoring.