Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW-RQA features. As validation, the CTW-RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW-RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW-RQA features effectively supplement the existing BCG features for AF detection.

Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal.

BACKGROUND: Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface. Recently, ballistocardiogram (BCG) has been investigated for AF diagnosis, which is an unobstructive and convenient technique to monitor heart activity in daily life. However, here is a lack of high-dimension representation and deep learning analysis of BCG. METHOD: Therefore, this paper proposes an attention-based multi-scale features fusion method by using BCG signal. The 1-D morphology feature extracted from Bi-LSTM network and 2-D rhythm feature extracted from reconstructed phase space are integrated by means of CNN network to improve the robustness of AF detection. To the best of our knowledge, this is the first study where the phase space trajectory of BCG is conducted. RESULTS: 2000 segments (AF and NAF) of BCG signals were collected from 59 volunteers suffering from paroxysmal AF in this survey. Compared to the classical time and frequency features and the state-of-the-art energy features with the popular machine learning classifiers, AF detection performance of the proposed method is superior, which has 0.947 accuracy, 0.935 specificity, 0.959 sensitivity, and 0.937 precision, for the same BCG dataset. The experimental results show that combined feature could excavate more potential characteristics, and the attention mechanism could enhance the pertinence for AF recognition. CONCLUSIONS: The proposed method can provide an innovative solution to capture the diverse scale descriptions of BCG and explore ways to involve the deep learning method to accurately screen AF in routine life.

The role of ultrasonography in the diagnosis of talocalcaneal coalitions.

BACKGROUND: Patients with talocalcaneal coalitions (TCC) often undergo computed tomography (CT). However, ultrasonography diagnosis of TCC has been seldom done according to the literature. PURPOSE: To investigate the accuracy of ultrasonography in diagnosing TCC compared to CT. MATERIAL AND METHODS: Ninety-seven consecutive patients with a clinical suspicion of TCC were included. Ultrasonography was used to assess the classification and complication of TCC. The main sonographic criteria for a positive diagnosis in cases of osseous coalition were the joint space between the medial surface of talar head and the underlying sustentaculum tali of calcaneus disappearing and being replaced by a continuous hyperechoic bony structure. In cases of fibrous coalition, ultrasonography revealed a reduced space of the joint associated with an irregular, angular appearance of its outline and hypoechoic fibrous tissue inside. These data were compared with CT findings. κ statistic was applied to determine the level of agreement. The sensitivity, specificity, positive and negative predictive values, accuracy, and Youden index of ultrasonography as a diagnostic method were assessed. RESULTS: Ultrasonography findings were positive in 20 of 97 patients with a clinical suspicion of TCC. The diagnosis was confirmed by CT in 21 patients. There were one false-positive result and two false-negative results by ultrasonography. The κ value was 0.907. The sensitivity, specificity, positive and negative predictive values, accuracy, and Youden index of ultrasonography were 90.5%, 98.7%, 95.0%, 97.4%, 96.9%, and 0.892, respectively. CONCLUSION: Ultrasonography could be a reliable, accurate, and non-radioactive diagnostic imaging method in diagnosis of patients with suspected TCC.