Efficacy of informed versus uninformed physiotherapy on postoperative retear rates of medium-sized and large rotator cuff tears.

BACKGROUND: It is important to perform the first 12 weeks of rehabilitation without risk of tearing a cuff tendon from its repair site. Our hypothesis was that performing early postoperative rehabilitation with a limitable pendulum exercise device can produce lower retear rate outcomes when it is combined with safe, informed physiotherapy compared with a standardized protocol of rehabilitation performed at home. METHODS: By using an asymmetric arm support brace and an advanced accelerometer, we attempted to determine the benefits of small pendulum exercises (proposed by Long et al). This study enrolled 24 patients to use a monitoring device in standardized small pendulum exercises. Clinical outcomes and magnetic resonance images were evaluated preoperatively and 12 weeks after surgery. RESULTS: While a patient performed pendulum exercises, a therapist used computer imagery to observe whether vertical acceleration was over a given threshold (identified as physiologic tremors), as a warning of and precaution associated with the increased risk of repair failure. Similar self-reported functional outcomes were reported in 2 areas. The rate of recurrent tears was significantly higher for both the medium-sized and large areas in the uninformed home rehabilitation group compared with the informed group. CONCLUSION: The results of monitoring of pendulum exercises to develop informed physical therapeutic methodology were consistent with those of previously published literature. In this study, use of a monitoring device during early rehabilitation was associated with lower retear rates after rotator cuff repair.

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.