Single-lead ECG based autonomic nervous system assessment for meditation monitoring.

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

OBJECTIVE: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed. However, as they were unsuitable for clinical application due to their low accuracy, we developed a new seizure detection algorithm using machine learning for single-channel EEG to overcome these limitations. METHODS: The dataset applied in our algorithm contains EEG recordings from human neonates. A 19-channel EEG system recorded the brain waves of 79 term neonates admitted to the NICU at the Helsinki University Hospital. From these datasets, we selected six patients with conformational seizure annotations for the pilot study and allocated four and two patients for our training and testing datasets, respectively. The presence of seizures in the EEGs was annotated independently by three experts through visual interpretation. We divided the data into epochs of 5 s each and further defined a seizure block to label the annotations from each expert recorded every second. Subsequently, to create a balanced dataset, any data point with a non-seizure label was moved to the training and test dataset. RESULT: The developed principal component feature-extracted machine learning algorithm used 62.5% of the relative time (only 5 s for decision) of the baseline, reaching an area under the ROC curve score of 0.91. The effect of diversified parameters was meticulously examined, and 100 principal components were extracted to optimize the model performance. CONCLUSION: Our machine learning-based seizure detection algorithm exhibited the potential for clinical application in NICUs, general wards, and at home and proved its convenience by requiring only a single channel for implementation.

Performance of the nasal photoplethysmographic index as an analgesic index during surgery under general anaesthesia.

In a previous study, we developed a new analgesic index using nasal photoplethysmography (nasal photoplethysmographic index, NPI) and showed that the NPI was superior to the surgical pleth index (SPI) in distinguishing pain above numerical rating scale 3. Because the NPI was developed using data obtained from conscious patients with pain, we evaluated the performance of NPI in comparison with the SPI and the analgesia nociception index (ANI) in patients under general anaesthesia with target-controlled infusion of propofol and remifentanil. The time of nociception occurrence was defined as when the signs of inadequate anaesthesia occurred. The median values of NPI, SPI, and ANI for 1 minute from the time of the sign of inadequate anaesthesia were determined as the value of each analgesic index that represents inadequate anaesthesia. The time of no nociception was determined as 2 minutes before the onset of skin incision, and the median value for 1 minute from that time was defined as the baseline value. In total, 81 patients were included in the analysis. NPI showed good performance in distinguishing inadequate anaesthesia during propofol-remifentanil based general anaesthesia. NPI had the highest value in terms of area under the receiver operating characteristic curve, albeit without statistical significance (NPI: 0.733, SPI: 0.722, ANI: 0.668). The coefficient of variations of baseline values of NPI, SPI, and ANI were 27.5, 47.2, and 26.1, respectively. Thus, the NPI was effective for detecting inadequate anaesthesia, showing similar performance with both indices and less baseline inter-individual variability than the SPI.

Real-Time Noninvasive Intracranial State Estimation Using Unscented Kalman Filter.

Intracranial pressure (ICP) monitoring is desirable as a first-line measure to assist decision-making in cases of increased ICP. Clinically, non-invasive ICP monitoring is also required to avoid infection and hemorrhage in patients. The relationships among the arterial blood pressure (ABP), ICP, cerebral blood flow, and its velocity ( [Formula: see text]) measured by transcranial Doppler ultrasound measurement have been reported. However, real-time non-invasive ICP estimation using these modalities is less well documented. This paper presents a novel algorithm for real-time and non-invasive ICP monitoring with [Formula: see text] and ABP, called direct-current (DC)-ICP. The technique was compared with invasive ICP for 10 acute-brain-injury patients admitted to Cheju Halla Hospital and Gangnam Severance Hospital from July 2017 to June 2018. The inter-subject correlation coefficient between true and estimate was 0.75 and the AUCs of the ROCs for prediction of increased ICP for the DC-ICP methods were 0.83. Thus, [Formula: see text] monitoring can facilitate reliable real-time ICP tracking with our novel DC-ICP algorithm, which can provide valuable information under clinical conditions.

Gesture Classification from Compressed EMG Based on Compressive Covariance Sensing.

Electromyogram (EMG) based human computer interface (HCI) is an attractive technique to monitor a patient, control an artificial arm, or play a game. Since EMG processing requires high sampling and transmission rates, a compression technique is important to implement an ultra-low power wireless EMG system. Previous study has a limitation due to the complexity of algorithm and the non-sparsity nature of EMG. In this study, we proposed a new EMG compression scheme based on a compressive covariance sensing (CCS). The covariance recovered from compressed EMG was used to classify user's gestures. The proposed method was verified with NinaPro open source data, which contains 49 gestures with 6 times repetition. As a result, the proposed CCS based EMG compression technique showed good covariance recovery performance and high classification rate as well as superior compression rate.

Development of 3D-printed myoelectric hand orthosis for patients with spinal cord injury.

BACKGROUND: Spinal cord injury (SCI) is a severe medical condition affecting the hand and locomotor function. New medical technologies, including various wearable devices, as well as rehabilitation treatments are being developed to enhance hand function in patients with SCI. As three-dimensional (3D) printing has the advantage of being able to produce low-cost personalized devices, there is a growing appeal to apply this technology to rehabilitation equipment in conjunction with scientific advances. In this study, we proposed a novel 3D-printed hand orthosis that is controlled by electromyography (EMG) signals. The orthosis was designed to aid the grasping function for patients with cervical SCI. We applied this hand exoskeleton system to individuals with tetraplegia due to SCI and validated its effectiveness. METHODS: The 3D architecture of the device was designed using computer-aided design software and printed with a polylactic acid filament. The dynamic hand orthosis enhanced the tenodesis grip to provide sufficient grasping function. The root mean square of the EMG signal was used as the input for controlling the device. Ten subjects with hand weakness due to chronic cervical SCI were enrolled in this study, and their hand function was assessed before and after wearing the orthosis. The Toronto Rehabilitation Institute Hand Function Test (TRI-HFT) was used as the primary outcome measure. Furthermore, improvements in functional independence in daily living and device usability were evaluated. RESULTS: The newly developed orthosis improved hand function of subjects, as determined using the TRI-HFT (p < 0.05). Furthermore, participants obtained immediate functionality on eating after wearing the orthosis. Moreover, most participants were satisfied with the device as determined by the usability test. There were no side effects associated with the experiment. CONCLUSIONS: The 3D-printed myoelectric hand orthosis was intuitive, easy to use, and showed positive effects in its ability to handle objects encountered in daily life. This study proved that combining simple EMG-based control strategies and 3D printing techniques was feasible and promising in rehabilitation engineering. TRIAL REGISTRATION: Clinical Research Information Service (CRiS), Republic of Korea. KCT0003995. Registered 2 May 2019 - Retrospectively registered.

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering.

So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

Non-invasive detection of intracranial hypertension using a simplified intracranial hemo- and hydro-dynamics model.

BACKGROUND: Monitoring of intracranial pressure (ICP) is highly important for detecting abnormal brain conditions such as intracranial hemorrhage, cerebral edema, or brain tumor. Until now, the monitoring of ICP requires an invasive method which has many disadvantages including the risk of infections, hemorrhage, or brain herniation. Therefore, many non-invasive methods have been proposed for estimating ICP. However, these methods are still insufficient to estimate sudden increases in ICP. METHODS: We proposed a simplified intracranial hemo- and hydro-dynamics model that consisted of two simple resistance circuits. From this proposed model, we designed an ICP estimation algorithm to trace ICP changes. First, we performed a simulation based on the original Ursino model with the real arterial blood pressure to investigate our proposed approach. We subsequently applied it to experimental data that were measured during the Valsalva maneuver (VM) and resting state, respectively. RESULTS: Simulation result revealed a small root mean square error (RMSE) between the estimated ICP by our approach and the reference ICP derived from the original Ursino model. Compared to the pulsatility index (PI) based approach and Kashif's model, our proposed method showed more statistically significant difference between VM and resting state. CONCLUSION: Our proposed method successfully tracked sudden ICP increases. Therefore, our method may serve as a suitable tool for non-invasive ICP monitoring.

Real-time estimation of respiratory rate from a photoplethysmogram using an adaptive lattice notch filter.

BACKGROUND: Many researchers have attempted to acquire respiratory rate (RR) information from a photoplethysmogram (PPG) because respiration affects the waveform of the PPG. However, most of these methods were difficult to operate in real-time because of their complexity or computational requirements. From these needs, we attempted to develop a method to estimate RR from a PPG with a light computational burden. METHODS: To obtain RR information, we adopt a sequential filtering structure and frequency estimation technique, which extracts a dominant frequency from a given signal. In particular, we used an adaptive lattice notch filter (ALNF) to estimate RR from a PPG along with an additional heart rate that is utilized as an adaptation parameter of our method. Furthermore, we designed a sequential infinite impulse response (IIR) notch filtering system (i.e., harmonic IIR notch filter) to eliminate the cardiac component and its harmonics from the PPG. We compared the proposed method with Burg's AR modeling method, which is widely used to estimate RR from a PPG, using open-source data and measured data. RESULTS: By using a statistical test, it was determined that our adaptive lattice-type respiratory rate estimator (ALRE) was significantly more accurate than Burg's AR model method (p <0.0001). Furthermore, the ALRE's tracking performance was better than that of Burg's method, and the variances of its estimates were smaller than those of Burg's method. CONCLUSIONS: In short, our method showed a better performance than Burg's AR modeling method for real-time applications.