A Heartbeat Classifier for Continuous Prediction Using a Wearable Device.

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.

Structure of a New Palatal Plate and the Artificial Tongue for Articulation Disorder in a Patient with Subtotal Glossectomy.

A palatal augmentation prosthesis (PAP) is used to facilitate improvement in the speech and swallowing functions of patients with tongue resection or tongue movement disorders. However, a PAP's effect is limited in cases where articulation disorder is severe due to wide glossectomy and/or segmental mandibulectomy. In this paper, we describe speech outcomes of a patient with an articulation disorder following glossectomy and segmental mandibulectomy. We used a palatal plate (PP) based on a PAP, along with an artificial tongue (KAT). Speech improvement was evaluated by a standardized speech intelligibility test consisting of 100 syllables. The speech intelligibility score was significantly higher when the patient wore both the PP and KAT than when he wore neither (p＝0.013). The conversational intelligibility score was significantly improved with the PP and KAT than without PP and KAT (p＝0.024). These results suggest that speech function can be improved in patients with hard tissue defects with segmental mandibulectomy using both a PP and a KAT. The nature of the design of the PP and that of the KAT will allow these prostheses to address a wide range of tissue defects.

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images.

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence.

Case Report: Virtual reality training for phantom limb pain after amputation.

Several reports have demonstrated the effectiveness of neurorehabilitation, such as mirror therapy or virtual reality, in treating phantom limb pain (PLP). This case study describes the effect of virtual reality training (VRT) on severe, long-term PLP and upper limb activity on the amputated side in a patient who underwent digit amputation 9 years prior. A woman in her 40 s underwent amputation of 2-5 fingers 9 years prior due to a workplace accident. She experienced persistent pain in the palms of her hand near the amputation sites. A single case design (ABA'B') was applied. Periods A and A' were set as periods without VRT intervention, and Periods B and B' were set as periods with VRT intervention. Periods A, B, A', and B' lasted 4, 10, 8, and 10 weeks, respectively. VRT was a task during which visual stimulation and upper limb movements were linked. The task consisted of catching a rolling ball in the display with a virtual hand, operated with both hands using a controller. VRT was performed once every 2-4 weeks for 30 min. Pain intensity was assessed using the short-form McGill Pain Questionnaire-2. Bilateral upper limb activity was measured continuously for 24 h using a triaxial accelerometer attached to the right and left wrist joints. The pain intensity was 147/220 points during Period A, 128 points during Period B, 93 points during Period A', and 100 points during Period B', showing a gradual decrease. Upper limb activity occurred mainly on the intact side during Periods A and B, whereas the activity on the amputated side increased 2-fold after Period A', and both upper extremities were used equally. Virtual reality training resulted in reduced pain intensity and increased activity in the upper limb. VRT may have induced reintegration of the sensory-motor loop, leading to a decrease in the PLP intensity. The upper limb activity on the amputated side may have also increased with the pain reduction. These results suggest that VRT may be valuable in reducing severe, long-term PLP.

Parieto-Occipital Alpha and Low-Beta EEG Power Reflect Sense of Agency.

The sense of agency (SoA) is part of psychophysiological modules related to the self. Disturbed SoA is found in several clinical conditions, hence understanding the neural correlates of the SoA is useful for the diagnosis and determining the proper treatment strategies. Although there are several neuroimaging studies on SoA, it is desirable to translate the knowledge to more accessible and inexpensive EEG-based biomarkers for the sake of applicability. However, SoA has not been widely investigated using EEG. To address this issue, we designed an EEG experiment on healthy adults (n = 15) to determine the sensitivity of EEG on the SoA paradigm using hand movement with parametrically delayed visual feedback. We calculated the power spectral density over the traditional EEG frequency bands for ten delay conditions relative to no delay condition. Independent component analysis and equivalent current dipole modeling were applied to address artifact rejection, volume conduction, and source localization to determine the effect of interest. The results revealed that the alpha and low-beta EEG power increased in the parieto-occipital regions in proportion to the reduced SoA reported by the subjects. We conclude that the parieto-occipital alpha and low-beta EEG power reflect the sense of agency.

Nonimmersive virtual reality mirror visual feedback therapy and its application for the treatment of complex regional pain syndrome: an open-label pilot study.

OBJECTIVE: Chronic pain conditions such as phantom limb pain and complex regional pain syndrome are difficult to treat, and traditional pharmacological treatment and invasive neural block are not always effective. Plasticity in the central nervous system occurs in these conditions and may be associated with pain. Mirror visual feedback therapy aims to restore normal cortical organization and is applied in the treatment of chronic pain conditions. However, not all patients benefit from this treatment. Virtual reality technology is increasingly attracting attention for medical application, including as an analgesic modality. An advanced mirror visual feedback system with virtual reality technology may have increased analgesic efficacy and benefit a wider patient population. In this preliminary work, we developed a virtual reality mirror visual feedback system and applied it to the treatment of complex regional pain syndrome. DESIGN: A small open-label case series. Five patients with complex regional pain syndrome received virtual reality mirror visual feedback therapy once a week for five to eight sessions on an outpatient basis. Patients were monitored for continued medication use and pain intensity. RESULTS: Four of the five patients showed >50% reduction in pain intensity. Two of these patients ended their visits to our pain clinic after five sessions. CONCLUSION: Our results indicate that virtual reality mirror visual feedback therapy is a promising alternative treatment for complex regional pain syndrome. Further studies are necessary before concluding that analgesia provided from virtual reality mirror visual feedback therapy is the result of reversing maladaptive changes in pain perception.