Tinnitus is a common clinical symptom. Researches have shown that fractal sound can effectively treat tinnitus. But current fractal sound is usually synthesized based on constant notes via fractal algorithm, which lead to monotony of synthesized fractal sound. So it is difficult to achieve personalized match. Clinical datas have confirmed that it is common to match tinnitus sound with nature sound and it has a good effect on regulating negative emotion and relieving tinnitus via some natural sound. Therefore, a new method of personalized synthesizing tinnitus rehabilitation sound based on iterative function system (IFS) fractal algorithm is proposed in this paper. This method firstly generates personalized audio library based on natural sound, then tinnitus rehabilitation sound is synthesized via IFS fractal algorithm. Simulation results show that rehabilitation sound in this paper can meet the basic requirements of tinnitus therapy sound and can match tinnitus sound by controlling personalized audio library. So it has reference significance to the treatment of tinnitus sound therapy.
ObjectiveTo explore the application of Tsetlin Machine (TM) in heart beat classification. MethodsTM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electrocardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. ResultsThe classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. ConclusionTM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.
Objective To propose a heart sound segmentation method based on multi-feature fusion network. Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset (a total of 3 153 recordings from 764 patients, about 91.93% of whom were male, with an average age of 30.36 years). Firstly the features were extracted in time domain and time-frequency domain respectively, and reduced redundant features by feature dimensionality reduction. Then, we selected optimal features separately from the two feature spaces that performed best through feature selection. Next, the multi-feature fusion was completed through multi-scale dilated convolution, cooperative fusion, and channel attention mechanism. Finally, the fused features were fed into a bidirectional gated recurrent unit (BiGRU) network to heart sound segmentation results. Results The proposed method achieved precision, recall and F1 score of 96.70%, 96.99%, and 96.84% respectively. Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance, which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.
ObjectiveTo use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. MethodsA total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. ResultsThe voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. ConclusionMachine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.
Objective To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.
Objective To investigate the effectiveness of HoloSight Orthopaedic Trauma Surgery Robot-assisted infra-acetabular screw placement for treatment of acetabular fractures. Methods The clinical data of 23 patients with acetabular fractures treated with open reduction and internal fixation and infra-acetabular screw placement in two medical centers between June 2022 and October 2023 were retrospectively analyzed. According to the the method of infra-acetabular screw placement, the patients were divided into navigation group (10 cases, using HoloSight Orthopaedic Trauma Surgery Robot-assisted screw placement) and freehand group (13 cases, using traditional X-ray fluoroscopy to guide screw placement). There was no significant difference in gender, age, body mass index, cause of injury, time from injury to operation, and Judet-Letournel classification between the two groups (P>0.05). The time of infra-acetabular screw placement, the fluoroscopy frequency, the guide pin adjustment times, the quality of screw placement, the quality of fracture reduction, and the function of hip joint were compared between the two groups. ResultsAll patients completed the operation successfully. The time of screw placement, the fluoroscopy frequency, and guide pin adjustment times in the navigation group were significantly less than those in the freehand group (P<0.05). The quality of screw placement in the navigation group was significantly better than that in the freehand group (P<0.05). Patients in both groups were followed up 6-11 months, with an average of 7.7 months. There were 9 and 9 cases in the navigation group and the freehand group who achieved excellent and good fracture reduction quality at 1 week after operation, and 12 and 12 cases with excellent and good hip joint function at last follow-up, respectively, and there was no significant difference between the two groups (P>0.05). The fractures in both groups healed well, and there was no significant difference in healing time (P>0.05). During the follow-up, there was no complication related to screw placement, such as failure of internal fixation, vascular and nerve injury, incisional hernia. ConclusionIn the treatment of acetabular fractures, compared with the traditional freehand screw placement, the HoloSight Orthopaedic Trauma Surgery Robot-assisted screw placement can reduce the time of screw placement, improve the accuracy of screw placement, and reduce the amount of radiation, which is an efficient, accurate, and safe surgical method.