Surgical Treatment of Ruptured Aneurysms of Lateral Spinal Artery Presenting as Intracranial Subarachnoid Hemorrhage : Case Series and Literature Review.

Lateral spinal artery (LSA) aneurysms are extremely rare lesions that can rupture and cause subarachnoid hemorrhage (SAH) even though the spinal arteries communicate directly with the subarachnoid space. To date, six cases of LSA aneurysms have been reported in the literature. (Table 1) Herein, three such cases are reported. All patients presented to the emergency department with headaches. The patients in the first two cases were confirmed to have SAH and LSA aneurysms on a brain computed tomography (CT) angiography performed at the hospital. Two patients had prior instances of cerebral infarction and coronary disease, respectively, and were undergoing antiplatelet therapy. The antiplatelet medication was halted for 2 weeks and 1 weeks, respectively, while conservative care was provided. Subsequently, a suboccipital craniectomy was performed, followed by aneurysm clipping. Following the surgery, both patients were discharged without any significant neurological deficits. Regarding the third patient, no aneurysm was found on brain CT angiography, and cerebral angiography was performed during the patient's hospital stay. She was hospitalized, where she received medication and conservative care, and was discharged with an improvement in bleeding without neurological symptoms. Subsequently, an LSA aneurysm was identified on a brain CT angiography performed at an outpatient clinic; however, the patient opted for treatment and was transferred to another hospital. LSA aneurysms are difficult to visualize using CT angiography; therefore, careful angiographic studies are required. Surgical clipping is the treatment of choice if the aneurysm is inaccessible by the endovascular treatment.

Ultra-Low-Power Wearable Vibration Sensor with Highly Accurate Embedded Classifier.

Reducing the power consumption of wearable sensors is a very important issue in relation to the device usage time and form factor. However, continuous wireless communication to analyze the measured signal in real-time significantly increases the power consumption of the wearable sensor. In this study, we propose a wearable vibration sensor that operates with extremely low power through an embedded signal classifier, which exhibits high accuracy and low calculation load. We demonstrate cough detection through the proposed sensor system. The result exhibits an accuracy of 93.0%, which is 24.3% higher than the conventional embedded classification algorithm. Also, the proposed approach reduces the average power consumption of the wearable sensor by 8.8 times. Clinical Relevance-People can measure the vibration from the body using an ultra-low-power wearable sensor. It provides a solution to automatically monitor cough symptoms in numerous patients.

Study on Optimal Position and Covering Pressure of Wearable Neck Microphone for Continuous Voice Monitoring.

Vocal cord disorder is one of the important health problems, especially in noisy industrial sites where excessive voice is required. A convenient and reliable communication method is required in a noisy environment to prevent the related disorders. However, the signal sensitivity of previous neck microphones is still insufficient to accurately convey the voice. In this study, we developed a skin-attachable neck microphone with a lightweight and flexible form factor. Also, we optimized the attachment position and covering pressure to maximize the signal sensitivity. As a result, we obtained the optimal position near the thyroid cartilage and confirmed that the signal sensitivity is the highest when the covering pressure is approximately 4 mmHg.Clinical Relevance- People can measure the voice status using a wearable neck microphone at the optimal position and covering pressure. It provides a solution to keep the vocal cords in good health even in a noisy environment.

A Wearable System for Heart Rate Recovery Evaluation with Real-Time Classification on Exercise Condition.

Heart rate recovery (HRR) is a convenient index to assess a cardiovascular autonomic function response to physical exercise. HRR monitoring during daily exercise can be an effective way to verify cardiorespiratory performance. Because HRR varies depending on exercise intensity and resting condition, an exercise condition needs to be acquired for a reliable HRR analysis. This study presents a wearable system for HRR evaluation with automatic labeling of exercise conditions using real-time activity classification. We developed an activity classification algorithm using two features from accelerometer sensor: an acceleration peak and an angle tilt peak. The classification algorithm was applied to a chest-attached wearable device with an embedded electrocardiogram sensor and accelerometer sensors. We classified daily activities such as running, walking, and postural transitions performed under supervised conditions. The wearable device system accurately detected activities with a sensitivity of 99.2 % and posture transitions with a sensitivity of 92 % and specificity of 93.3 % for seven healthy subjects. The proposed wearable system can help monitor HRR during exercise training by labeling the exercise condition simultaneously.