松浦 広昂

BACKGROUND: Magnetic stimulation devices can be large because of the need for cooling systems. We developed a compact and lightweight Spinning Permanent Magnet (SPM) device that generates magnetic fields with intensities below the motor threshold. In this report, we present the case of a post-stroke patient in which an immediate reduction in spasticity of the ankle plantar flexors was achieved after SPM treatment. CASE: A 37-year-old man was admitted to our hospital with a right putamen hemorrhage. The patient underwent conservative therapy and exhibited residual left hemiplegia and spasticity. Three months after stroke onset, he was able to walk with supervision while using a left ankle-foot orthosis and a T-cane. The Modified Ashworth Scale (MAS) score of the left ankle plantar flexors was 1+. The plantar flexors were stimulated by SPM treatment. The outcomes were the Hmax/Mmax of the tibial nerve (soleus muscle) and the MAS score. On the first day, SPM stimulation was applied for 30 min. On the second day, a sham stimulation of the same duration was performed. On the third day, the SPM stimulation was repeated. Hmax/Mmax decreased from 41.5% to 37.7% on the first day, and from 46.9% to 31.6% on the third day after SPM stimulation. The MAS score decreased from 1+ to 1 on both days. In contrast, after sham stimulation, Hmax/Mmax increased from 39.2% to 44.2%, whereas the MAS score remained unchanged at 1+. DISCUSSION: Stimulation below the motor threshold using SPM treatment can effectively reduce spasticity.

Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.