Helle K. Iversen

PHASE II TRIAL TO EVALUATE THE ACTIGAIT IMPLANTED DROP-FOOT STIMULATOR IN ESTABLISHED HEMIPLEGIA

OBJECTIVEnTo evaluate a selective implantable drop foot stimulator (ActiGait) in terms of effect on walking and safety.nnnDESIGNnA phase II trial in which a consecutive sample of participants acted as their own controls.nnnSUBJECTSnPeople who had suffered a stroke at least 6 months prior to recruitment and had a drop-foot that affected walking were recruited from 3 rehabilitation centres in Denmark.nnnMETHODSnStimulators were implanted into all participants. Outcome measures were range of ankle dorsiflexion with stimulation and maximum walking speed and distance walked in 4 minutes. Measurements were applied before implantation, at 90 days and at a long-term follow-up assessment. Changes over time and with and without stimulation are reported. Safety was evaluated by nerve conduction velocity and adverse events.nnnRESULTSnFifteen participants were implanted and 13 completed the trial. Long-term improvements were detected in walking speed and distance walked in 4 minutes when stimulated, and the orthotic effect of stimulation showed statistically significant improvement. The device did not compromise nerve conduction velocity and no serious device-related adverse events were reported. Technical problems were resolved by the long-term follow-up assessment at which further improvement in walking was observed.nnnCONCLUSIONnThis trial has evaluated the safety and performance of the device, which was well accepted by patients and did not compromise safety.

Patients' perceptions of the benefits and problems of using the ActiGait implanted drop-foot stimulator

OBJECTIVEnTo evaluate patients perceptions of the benefits and problems associated with using the ActiGait implanted drop-foot stimulator.nnnMETHODnThirteen participants who had suffered a stroke at least 6 months prior to recruitment, had a drop-foot that affected walking and had taken part in a trial in which an ActiGait drop-foot stimulator had been implanted, completed a postal questionnaire.nnnRESULTSnUsers agreed that the ActiGait had a positive effect on walking; they used it regularly and had little difficulty with putting it on and taking it off. Reliability was a greater problem at 90 days than at the final assessment. Ten of the 13 responders either agreed or strongly agreed with the statement that the ActiGait improved their quality of life at 90 days and 9 out of 12 at the final assessment: 11 of the 12 respondents would recommend the ActiGait to others.nnnDISCUSSION AND CONCLUSIONnFrom the users perspective the ActiGait improved walking, it was reported to be used regularly and it appeared to be easier to use than a surface system. Users were equivocal about the reliability of the system at 90 days, but at the final assessment reliability had improved.

Increased cerebrovascular pCO2 reactivity in migraine with aura--a transcranial Doppler study during hyperventilation.

Cerebrovascular reactivity during hypocapnia was tested in 20 migraineurs (8 with aura, 12 without aura) and 30 sexand age-matched healthy subjects, and during nitroglycerin-induced headache in 12 healthy subjects. Before and during hyperventilation, mean blood-flow velocity (Vmean) in the middle cerebral artery was measured with transcranial Doppler. In each subject a pCO2 reactivity index (RI) was calculated as DVmean/baseline Vmean)/ DpCO2. Interictally, patients with migraine with aura showed higher RI (p < 0.05 ANOVA and multiple range test) than controls, whereas migraineurs without aura did not differ from healthy subjects. Ictal and interictal RIs were similar in 9 patients suffering from migraine without aura. No side-to-side differences were detected in RI. During nitroglycerin-induced headache, the RIs were no different from those recorded during migraine attacks and in non-nitroglycerin-provoked healthy controls (p < 0.05, ANOVA and multiple range test). The exaggerated response in migraine with aura might predispose for the characteristic changes in rCBF seen during attacks.

Automatic Real-Time Embedded QRS Complex Detection for a Novel Patch-Type Electrocardiogram Recorder

Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. The design of novel patch-type ECG recorders has increased the accessibility of these long-term recordings. In many applications, it is furthermore an advantage for these devices that the recorded ECGs can be analyzed automatically in real time. The purpose of this study was therefore to design a novel algorithm for automatic heart beat detection, and embed the algorithm in the CE marked ePatch heart monitor. The algorithm is based on a novel cascade of computationally efficient filters, optimized adaptive thresholding, and a refined search back mechanism. The design and optimization of the algorithm was performed on two different databases: The MIT-BIH arrhythmia database (Se = 99.90%, P+ = 99.87) and a private ePatch training database (Se = 99.88%, P+ = 99.37%). The offline validation was conducted on the European ST-T database (Se = 99.84%, P+ = 99.71%). Finally, a double-blinded validation of the embedded algorithm was conducted on a private ePatch validation database (Se = 99.91%, P+ = 99.79%). The algorithm was thus validated with high clinical performance on more than 300 ECG records from 189 different subjects with a high number of different abnormal beat morphologies. This demonstrates the strengths of the algorithm, and the potential for this embedded algorithm to improve the possibilities of early diagnosis and treatment of cardiovascular diseases.

Altered low frequency oscillations of cortical vessels in patients with cerebrovascular occlusive disease - a NIRS study

Analysis of cerebral autoregulation by measuring spontaneous oscillations in the low frequency spectrum of cerebral cortical vessels might be a useful tool for assessing risk and investigating different treatment strategies in carotid artery disease and stroke. Near infrared spectroscopy (NIRS) is a non-invasive optical method to investigate regional changes in oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) in the outermost layers of the cerebral cortex. In the present study we examined oxyHb low frequency oscillations, believed to reflect cortical cerebral autoregulation, in 16 patients with both symptomatic carotid occlusive disease and cerebral hypoperfusion in comparison to healthy controls. Each hemisphere was examined with two NIRS channels using a 3u2009cm source detector distance. Arterial blood pressure (ABP) was measured via a finger plethysmograph. Using transfer function analysis ABP-oxyHb phase shift and gain as well as inter-hemispheric phase shift and amplitude ratio were assessed. We found that inter-hemispheric amplitude ratio was significantly altered in hypoperfusion patients compared to healthy controls (Pu2009=u20090.010), because of relatively lower amplitude on the hypoperfusion side. The inter-hemispheric phase shift showed a trend (Pu2009=u20090.061) toward increased phase shift in hypoperfusion patients compared to controls. We found no statistical difference between hemispheres in hypoperfusion patients for phase shift or gain values. There were no differences between the hypoperfusion side and controls for phase shift or gain values. These preliminary results suggest an impairment of autoregulation in hypoperfusion patients at the cortical level detected by NIRS.

An end-to-end deep learning approach to MI-EEG signal classification for BCIs

Abstract Goal: To develop and implement a Deep Learning (DL) approach for an electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) system that could potentially be used to improve the current stroke rehabilitation strategies. Method: The DL model is using Convolutional Neural Network (CNN) layers for learning generalized features and dimension reduction, while a conventional Fully Connected (FC) layer is used for classification. Together they build a unified end-to-end model that can be applied to raw EEG signals. This previously proposed model was applied to a new set of data to validate its robustness against data variations. Furthermore, it was extended by subject-specific adaptation. Lastly, an analysis of the learned filters provides insights into how such a model derives a classification decision. Results: The selected global classifier reached 80.38%, 69.82%, and 58.58% mean accuracies for datasets with two, three, and four classes, respectively, validated using 5-fold crossvalidation. As a novel approach in this context, transfer learning was used to adapt the global classifier to single individuals improving the overall mean accuracy to 86.49%, 79.25%, and 68.51%, respectively. The global models were trained on 3s segments of EEG data from different subjects than they were tested on, which proved the generalization performance of the model. Conclusion: The results are comparable with the reported accuracy values in related studies and the presented model outperforms the results in the literature on the same underlying data. Given that the model can learn features from data without having to use specialized feature extraction methods, DL should be considered as an alternative to established EEG classification methods, if enough data is available.