Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Researc...Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.展开更多
Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the stru...Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.展开更多
文摘Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.
基金supported by the National Natural Science Foundation of China under Grant Nos.91120305,61272251the National Basic Research 973 Program of China under Grant No.2015CB856004
文摘Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.
