Low-frequency detection in ECG signals and joint EEG-Ergospirometric measurements for precautionary diagnosis

Lay-Ekuakille, A.; Vergallo, P.; Trabacca, Antonio; Rinaldis, Marta De; Angelillo, F.; Conversano, Francesco; Casciaro, Sergio

doi:10.1016/j.measurement.2012.05.024

Cited by 40 publications

(17 citation statements)

References 16 publications

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“…For the assessment of DBS side effects such as dyskinesia, paresthesia, and other non-motor symptoms, more parameters should be realized in the next version [37].…”

Section: Discussionmentioning

confidence: 99%

A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

et al. 2013

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Deep brain stimulation (DBS) surgery is most effective in reducing the symptoms of Parkinson's disease and essential tremor. At present, there is no designated instrumental method for measuring the immediate effects of DBS. This paper presents the concept of a glove monitoring system for DBS. With the benefits of microelectromechanical systems, inertial measurement unit, and force sensitive resistor (FSR), the system is portable and can be integrated into a textile glove. Tremors, bradykinesia, and rigidity assessments are performed by the system. Several test tasks are chosen to be performed during DBS surgery to evaluate the electrode's position and stimulation intensity. Each quantified symptom severity of the patient is added to a list shown in the graphical user interface for comparison. Comparative experiments between the prototype and an electromagnetic motion tracking system were presented. The FSR boxes were validated with weights. Experimental results show that this system is reliable for tremor amplitude determination, movement angles measurement, and resistance measurement to a passive movement. In addition, it can be found that inconsistent tremor movements have an influence on the tremor amplitude calculation done with power spectral density estimation.Index Terms-MEMS IMU, glove monitoring system, parkinson's disease quantitative assessment, tremor, bradykinesia, rigidity, reliability testing.

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“…For the assessment of DBS side effects such as dyskinesia, paresthesia, and other non-motor symptoms, more parameters should be realized in the next version [37].…”

Section: Discussionmentioning

confidence: 99%

A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

et al. 2013

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“…[10][11][12][13][14][15][16][17][18][19][20][21][22] Complexity reduction and fast convergence of the noise cancelation algorithm have remained a topic of intense research. This is because of the fact that with increase in the data transmission rate increases the order of filter.…”

Section: Introductionmentioning

confidence: 99%

Efficient block processing of long duration biotelemetric brain data for health care monitoring

Soumya

Rahman

Reddy

et al. 2015

Review of Scientific Instruments

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In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

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“…Therefore, many attempts have been performed to use only EEGs to reveal neurological pathologies; even if additional information are required to complete data reliability using other instrumentation such as electrocardiogram and ergospirometry [3]- [5]. In current clinical analysis, the doctor performs a visual and experienced-based analysis of EEG signals in the time domain, but a great amount of data are required to diagnose brain abnormalities, and in addition, the visual inspection of brain rhythms in the EEG signal is subjective and a complete statistical analysis is not always possible.…”

mentioning

confidence: 99%

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Lay-Ekuakille

Vergallo

Griffo

et al. 2014

IEEE Trans. Instrum. Meas.

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Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies "in space and in time" giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patient's clinical condition.

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Low-frequency detection in ECG signals and joint EEG-Ergospirometric measurements for precautionary diagnosis

Cited by 40 publications

References 16 publications

A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

Efficient block processing of long duration biotelemetric brain data for health care monitoring

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

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