Gait Study of Parkinson's Disease Subjects Using Haptic Cues with A Motorized Walker.

Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson's disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course under different haptic speed cue conditions (1) without assistance, (2) pushing a conventional rolling walker, and (3) holding onto a self-navigating motorized walker under different speed cues. Six people with PD were recruited at the New York Institute of Technology College of Osteopathic Medicine to participate in this study. Spatial posture and gait data of the test subjects were collected via a VICON motion capture system. We developed a framework to process and extract gait features and applied statistical analysis on these features to examine the significance of the findings. The results showed that the motorized walker providing a robust haptic cue significantly improved gait symmetry of PD subjects. Specifically, the asymmetry index of the gait cycle time was reduced from 6.7% when walking without assistance to 0.56% and below when using a walker. Furthermore, the double support time of a gait cycle was reduced by 4.88% compared to walking without assistance.

Classification and visualization tool for gait analysis of Parkinson's disease.

There is no standard diagnostic test for Parkinson's Disease. In this paper, we propose a data mining based statistical diagnostic method towards a standard and accurate diagnostic test. The result shows the proposed regression formula, which only requires patient's stride data, can provide a 90% accuracy to diagnose PD patients.

EEG analysis via multiscale Lempel-Ziv complexity for seizure detection.

Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.

Seizure detection methods using a cascade architecture for real-time implantable devices.

Implantable high-accuracy, and low-power seizure detection is a challenge. In this paper, we propose a cascade architecture to combine different seizure detection algorithms to optimize power and accuracy of the overall seizure detection system. The proposed architecture consists of a cascade of two seizure detection stages. In the first-stage detector, a lightweight (low-power) algorithm is used to detect seizure candidates with the understanding that there will be a high number of false positives. In the second-stage detector-and only for the seizure candidates detected in the first detector-a high-accuracy algorithm is used to eliminate the false positives. We show that the proposed cascade architecture can reduce power consumption of seizure detection by 80% with high accuracy, offering a suitable option for real-time implantable seizure detectors.

Compression-ratio-based seizure detection.

For wireless seizure monitoring devices seizure detection and data compression are two critical tasks that need to be carefully designed against a very tight power budget to maximize the battery life. These two tasks are usually considered separately and algorithms for each are developed separately. In this paper, we consider having a single low-power algorithm for implementing both seizure detection and data compression. Towards that end, we investigated compression ratio (CR) as a seizure marker and show that the seizure detection can be achieved as a by-product of compression with no additional cost, and thus overall system power can be reduced. We show that the proposed method, the CR-based seizure detection has promising performance with 88% seizure detection accuracy, and 5.5 false positives per hour (FPh) without any computation overhead.

Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data.

High-resolution Electrocorticography (HR-ECoG) has emerged as a key strategic technology for recording localized neural activity with high temporal and spatial resolution with potential applications in brain-computer interfaces (BCI), and seizure detection for epilepsy. However, HR-ECoG has 400 times the resolution of conventional ECoG, making it a challenge to process, transmit and store the HR-ECoG data. Therefore, simple and efficient compression algorithms are vital for the feasibility of implantable wireless medical devices for HR-ECoG recordings. In this paper, following the observation that HR-ECoG signals have both high spatial and temporal correlations similar to video/image signals, various compression methods suitable for video/image- compression based on motion estimation, discrete cosine transform (DCT) and discrete wavelet transform (DWT)- are investigated for compressing HR-ECoG data. We first simplify these methods to satisfy the low-power requirements for implantable devices. Then, we demonstrate that spatiotemporal compression methods produce up to 46% more data reduction on HR-ECoG data than compression methods using only spatial compression do. We further show that this data reduction can be achieved with low hardware complexity. In particular, among the methods investigated, spatiotemporal compression using DCT-based methods provide the best trade-off between hardware complexity and compression performance, and thus we conclude that DCT-based compression is a promising solution for ultralow-power implantable devices for HR-ECoG.

Optimizing analog-to-digital converters for sampling extracellular potentials.

In neural implants, an analog-to-digital converter (ADC) provides the delicate interface between the analog signals generated by neurological processes and the digital signal processor that is tasked to interpret these signals for instance for epileptic seizure detection or limb control. In this paper, we propose a low-power ADC architecture for neural implants that process extracellular potentials. The proposed architecture uses the spike detector that is readily available on most of these implants in a closed-loop with an ADC. The spike detector determines whether the current input signal is part of a spike or it is part of noise to adaptively determine the instantaneous sampling rate of the ADC. The proposed architecture can reduce the power consumption of a traditional ADC by 62% when sampling extracellular potentials without any significant impact on spike detection accuracy.

High-efficiency wireless power delivery for medical implants using hybrid coils.

With the exciting developments in the implant technology allowing sophisticated signal processing, stimulation, and drug delivery capabilities, there is new hope for many patients of epilepsy, Parkinson's disease, and stroke to improve their quality of life. Such implants require high power to deliver the promised rich functionality. Yet, delivering high power to implants without damaging the tissue due to heating while keeping the implant footprint small is a challenge. In this paper, we propose a hybrid multi-layer coil as the secondary coil to provide a power and space-efficient solution. The proposed coils can deliver power to an implant for long durations without increasing the skin temperature over 1C.

Transmeningeal muscimol can prevent focal EEG seizures in the rat neocortex without stopping multineuronal activity in the treated area.

Muscimol has potent antiepileptic efficacy after transmeningeal administration in animals. However, it is unknown whether this compound stops local neuronal firing at concentrations that prevent seizures. The purpose of this study was to test the hypothesis that epidurally administered muscimol can prevent acetylcholine (Ach)-induced focal seizures in the rat neocortex without causing cessation of multineuronal activity. Rats were chronically implanted with a modified epidural cup over the right frontal cortex, with microelectrodes positioned underneath the cup. In each postsurgical experimental day, either saline or 0.005-, 0.05-, 0.5- or 5.0-mM muscimol was delivered through the cup, followed by a 20-min monitoring of the multineuronal activity and the subsequent delivery of Ach in the same way. Saline and muscimol pretreatment in the concentration range of 0.005-0.05 mM did not prevent EEG seizures. In contrast, 0.5-mM muscimol reduced the average EEG Seizure Duration Ratio value from 0.30±0.04 to 0. At this muscimol concentration, the average baseline multineuronal firing rate of 10.9±4.4 spikes/s did not change significantly throughout the 20-min pretreatment. Muscimol at 5.0mM also prevented seizures, but decreased significantly the baseline multineuronal firing rate of 7.0±1.8 to 3.7±0.9 spikes/s in the last 10 min of pretreatment. These data indicate that transmeningeal muscimol in a submillimolar concentration range can prevent focal neocortical seizures without stopping multineuronal activity in the treated area, and thus this treatment is unlikely to interrupt local physiological functions.

Multi-layer coils for efficient Transcutaneous Power Transfer.

TETS (Transcutaneous Energy Transfer System) has been successfully used for powering medical implants for different purposes such as for neural recordings and drug delivery. Yet, due to their low power transfer efficiency, these devices can cause unacceptable increase in skin temperature limiting their scalability to high power levels. Although, the efficiency of these systems can be improved by increasing coil diameter, in many cases this is not practical due to strict physical constraints on the coil diameter. In this paper, we investigate using multi-layer coils as secondary coils in the TETS to increase the power transfer efficiency, and thus allowing the delivery of the desired power safely for a longer period. Our experiments show a 5× increase in the duration of safe power delivery (not increasing the skin temperature more than 2 C) using multi-layer coils as the secondary coil compared to using single-layer coils even when there is a 50% misalignment in between primary and secondary coils. This increase in duration of safe power transfer is shown to be over 16× more when the coils are aligned. The improvement in the duration of safe power transfer is achieved without increasing the coil diameter and with a coil thickness of 2 mm.