This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.
Humans ; Autonomic Nervous System/physiology* ; Algorithms ; Aging/physiology* ; Electrocardiography/methods* ; Heart Rate/physiology* ; Wavelet Analysis ; Aged ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

The pain-evoked potential electroencephalogram (EEG) is an effective electrophysiological indicator for pain assessment, yet its extraction is challenging due to interference from background activity and involuntary blinks. Although existing blink artifact-removal methods show efficacy, they face limitations such as the need for reference signals, neglect of individual differences, and reliance on user input, hindering their practical application in clinical pain assessments. In this paper, we propose a novel framework applying adaptive quadrature mirror filter banks (AQMFB) with discrete wavelet transform (DWT) to remove blink artifacts in pain EEG. Unlike traditional DWT methods that apply fixed wavelets across subjects, our method adapts wavelet construction based on the characteristics of EEG. Experimental results demonstrate that AQMFB-DWT outperforms four leading methods in removing blink artifacts with minimal distortion of pain information, all within an acceptable processing time. This technique is a valuable preprocessing step for enhancing the extraction of pain-evoked potentials.
Humans ; Artifacts ; Blinking/physiology* ; Electroencephalography/methods* ; Pain/diagnosis* ; Male ; Wavelet Analysis ; Adult ; Female ; Evoked Potentials/physiology* ; Young Adult ; Brain/physiopathology* ; Pain Measurement/methods* ; Signal Processing, Computer-Assisted

The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.
Humans ; Sleep Stages ; Algorithms ; Sleep ; Wavelet Analysis ; Electroencephalography/methods* ; Machine Learning

Bowel sounds is an important indicator to monitor and reflect intestinal motor function, and traditional manual auscultation requires high professional knowledge and rich clinical experience of doctors. In addition, long-time auscultation is time-consuming and laborious， which may lead to misjudgment caused by subjective error. To solve the problem, firstly, the wavelet transform is used to preprocess the bowel sounds signal for noise reduction and enhancement. Secondly, three typical features of intestinal sound were extracted. According to the combination of these features, a three-stage decision was designed to carry out multi-parameter and multi-feature joint threshold detection. This algorithm realized the detection of bowel sound signal and the location of its start and end points, making it possible that the complete bowel sound signal was extracted effectively. In this study, a large number of clinical data and label of bowel sounds were collected, and a new effective evaluation method was proposed to verify the proposed method. The accuracy rate is 83.51%. Results of this study will provide systematic support and theoretical guarantee for the diagnosis of intestinal diseases and the monitoring of postoperative intestinal function recovery of patients.
Algorithms ; Auscultation ; Humans ; Intestines ; Signal Processing, Computer-Assisted ; Wavelet Analysis

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.
Algorithms ; Child ; Deep Learning ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted ; Wavelet Analysis

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.
Algorithms ; Electroencephalography ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Wavelet Analysis

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network (ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia: awake, light anesthesia, moderate anesthesia and deep anesthesia (
Algorithms ; Anesthesia, General ; Electroencephalography ; Humans ; Neural Networks, Computer ; Wavelet Analysis

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
Algorithms ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis

The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.
Algorithms ; Arrhythmias, Cardiac ; Electrocardiography ; Heart Rate ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis