Acute myocardial ischemia monitoring before and during angioplasty by a novel vectorcardiographic parameter set.

BACKGROUND: This work evaluates the vectorcardiographic dynamic changes in ischemic patients before and during Percutaneous Transluminal Coronary Angioplasty (PTCA). METHODS: Four QRS-loop parameters were computed in 51 ischemic and 52 healthy subjects with the objective of assessing the vectorcardiographic differences between both groups: maximum vector magnitude (QRS(mVM)), planar area (QRS(PA)), maximum distance between centroid and loop (QRS(mDCL)) and perimeter (QRS(P)).The conventional ST-change vector magnitude (STC(VM)), QRS-vector difference (QRS(VD)) and spatial ventricular gradient (SVG) were also calculated. RESULTS: Statistical minute-by-minute PTCA comparison against a healthy population showed that ischemic patients monitoring is greatly enhanced when all the QRS-loop parameters, in combination with the standard STC(VM), QRS(VD) and SVG indexes, are used in the classification. Sensitivity and Specificity, in turn, reached rather high values, 95.4% and 95.2%, respectively. CONCLUSIONS: These new vectorcardiographic set of complementary QRS-loop parameters, when combined with the classics STC(VM), QRS(VD) and SVG indexes, increase sensitivity and specificity for acute ischemia monitoring.

Multisensory Stimulation and EEG Recording Below the Hair-Line: A New Paradigm on Brain Computer Interfaces.

To test the feasibility of implementing multisensory (auditory and visual) stimulation in combination with electrodes placed on non-hair positions to design more efficient and comfortable Brain-computer interfaces (BCI). Fifteen volunteers participated in the experiments. They were stimulated by visual, auditory and multisensory stimuli set at 37, 38, 39 and 40Hz and at different phases (0°, 90°, 180° and 270°). The electroencephalogram (EEG) was measured from Oz, T7, T8, Tp9 and Tp10 positions. To evaluate the amplitude of the visual and auditory evoked potentials, the signal-to-noise ratio (SNR) was used and the accuracy of detection was calculated using canonical correlation analysis. Additionally, the volunteers were asked about the discomfort of each kind of stimulus. The multisensory stimulation allows for attaining higher SNR on every electrode. Non-hair (Tp9 and Tp10) positions attained SNR and accuracy similar to the ones obtained from occipital positions on visual stimulation. No significant difference was found on the discomfort produced by each kind of stimulation. The results demonstrated that multisensory stimulation can help in obtaining high amplitude steady-state evoked responses with a similar discomfort level. Then, it is possible to design a more efficient and comfortable hybrid-BCI based on multisensory stimulation and electrodes on non-hair positions. The current article proposes a new paradigm for hybrid-BCI based on steady-state evoked potentials measured from the area behind-the-ears and elicited by multisensory stimulation, thus, allowing subjects to achieve similar performance to the one achieved by visual-occipital BCI, but measuring the EEG on a more comfortable electrode location.

Patient non-specific algorithm for seizures detection in scalp EEG.

Epilepsy is a brain disorder that affects about 1% of the population in the world. Seizure detection is an important component in both the diagnosis of epilepsy and seizure control. In this work a patient non-specific strategy for seizure detection based on Stationary Wavelet Transform of EEG signals is developed. A new set of features is proposed based on an average process. The seizure detection consisted in finding the EEG segments with seizures and their onset and offset points. The proposed offline method was tested in scalp EEG records of 24-48h of duration of 18 epileptic patients. The method reached mean values of specificity of 99.9%, sensitivity of 87.5% and a false positive rate per hour of 0.9.

Identification of Patients with Myocardial Infarction. Vectorcardiographic and Electrocardiographic Analysis.

BACKGROUND: The largest morbidity and mortality group worldwide continues to be that suffering Myocardial Infarction (MI). The use of vectorcardiography (VCG) and electrocardiography (ECG) has improved the diagnosis and characterization of this cardiac condition. OBJECTIVES: Herein, we applied a novel ECG-VCG combination technique to identifying 95 patients with MI and to differentiating them from 52 healthy reference subjects. Subsequently, and with a similar method, the location of the infarcted area permitted patient classification. METHODS: We analyzed five depolarization and four repolarization indexes, say: a) volume; b) planar area; c) QRS loop perimeter; d) QRS vector difference; e - g) Area under the QRS complex, ST segment and T-wave in the (X, Y, Z) leads; h) ST-T Vector Magnitude Difference; i) T-wave Vector Magnitude Difference; and j) the spatial angle between the QRS complex and the T-wave. For classification, patients were divided into two groups according to the infarcted area, that is, anterior or inferior sectors (MI-ant and MI-inf, respectively). RESULTS: Our results indicate that several ECG and VCG parameters show significant differences (p-value<0.05) between Healthy and MI subjects, and between MI-ant and MI-inf. Moreover, combining five parameters, it was possible to classify the MI and healthy subjects with a sensitivity = 95.8%, a specificity = 94.2%, and an accuracy = 95.2%, after applying a linear discriminant classifier method. Similarly, combining eight indexes, we could separate out the MI patients in MI-ant vs MI-inf with a sensitivity = 89.8%, 84.8%, respectively, and an accuracy = 89.8%. CONCLUSIONS: The new multivariable MI patient identification and localization technique, based on ECG and VCG combination indexes, offered excellent performance to differentiating populations with MI from healthy subjects. Furthermore, this technique might be applicable to estimating the infarcted area localization. In addition, the proposed method would be an alternative diagnostic technique in the emergency room.

Automatic detection of epileptic seizures in long-term EEG records.

Epilepsy is a neurological disorder which affects nearly 1.5% of the world׳s total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.

Attention-level transitory response: a novel hybrid BCI approach.

OBJECTIVE: People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). APPROACH: Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. MAIN RESULTS: The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. SIGNIFICANCE: A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

Improved alignment method for noisy high-resolution ECG and Holter records using multiscale cross-correlation.

The coherent signal averaging process requires accurate estimation of a fiducial point in all beats to be averaged. The temporal cross-correlation between each detected beat and a template beat is the typical alignment method used with high-resolution electrocardiogram (HRECG) records. However, this technique does not produce a precise fiducial mark in records with high noise levels, like those found in Holter HRECG systems. In this study, we propose a new alignment method based on the multiscale cross-correlation between the template and each detected beat. We report the results of tests comparing multiscale and temporal methods for 3000 beats of simulated HRECG records corrupted separately with white noise, electromyographic noise and power line interference (50 Hz) of different root mean square levels. A second study with simulated records constructed from real Holter HRECG records is also presented. The results indicate that the multiscale alignment method produces a lower trigger jitter than the temporal method in all tests. We conclude that the proposed alignment method can be used in HRECG records with high noise levels.

Automatic detection of drowsiness in EEG records based on multimodal analysis.

Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.

Novel technique for ST-T interval characterization in patients with acute myocardial ischemia.

BACKGROUND: The novel signal processing techniques have allowed and improved the use of vectorcardiography (VCG) to diagnose and characterize myocardial ischemia. Herein, we studied vectorcardiographic dynamic changes of ventricular repolarization in 80 patients before (control) and during Percutaneous Transluminal Coronary Angioplasty (PTCA). METHODS: We propose four vectorcardiographic ST-T parameters, i.e., (a) ST Vector Magnitude Area (aSTVM); (b) T-wave Vector Magnitude Area (aTVM); (c) ST-T Vector Magnitude Difference (ST-TVD), and (d) T-wave Vector Magnitude Difference (TVD). For comparison, the conventional ST-Change Vector Magnitude (STCVM) and Spatial Ventricular Gradient (SVG) were also calculated. RESULTS: Our results indicate that several vectorcardiographic parameters show significant differences (p-value<0.05) before starting and during PTCA. Statistical minute-by-minute PTCA comparison against the control situation showed that ischemic monitoring reached a sensitivity=90.5% and a specificity=92.6% at the 5th minute of the PTCA, when aSTVM and ST-TVD were used as classifiers. CONCLUSIONS: We conclude that the sensitivity and specificity for acute ischemia monitoring could be increased with the use of only two vectorcardiographic parameters. Hence, the proposed technique based on vectorcardiography could be used in addition to the conventional ST-T analysis for better monitoring of ischemic patients.

Novel set of vectorcardiographic parameters for the identification of ischemic patients.

New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detection of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemic patients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vectorcardiographic difference between both groups so leading to their subsequent classification. For that matter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; (c) Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and Optimum Plane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude (ST(VM)) was also calculated. Results indicate that several vectorcardiographic parameters show significant differences between healthy and ischemic subjects. The identification of ischemic patients via discriminant analysis using ST(VM) produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study, the QRS-loop parameter with the best global performance was Volume, which achieved Sens=64.5% and Spec=74.6%. However, when all QRS-loop parameters and ST(VM) were combined, we obtained Sens=88.5% and Spec=92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventional ST(VM) analysis in the identification of ischemic patients.

Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.

This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz). This high frequency stimulation scheme can reduce or even eliminate visual fatigue, allowing the user to achieve a stable performance for long term BCI operation. The BCI system uses power-spectral density analysis associated to three bipolar electroencephalographic channels. As the results show, 2 subjects were reported as SSVEP-BCI illiterates (not able to use the BCI), and, consequently, 13 subjects (12 of them healthy) could navigate the wheelchair in a room with obstacles arranged in four distinct configurations. Volunteers expressed neither discomfort nor fatigue due to flickering stimulation. A transmission rate of up to 72.5 bits/min was obtained, with an average of 44.6 bits/min in four trials. These results show that people could effectively navigate a robotic wheelchair using a SSVEP-based BCI with high frequency flickering stimulation.

SSVEP-BCI implementation for 37-40 Hz frequency range.

This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general. Offline results are presented, which corresponds to a correct classification rate of up to 99% and a Information Transfer Rate (ITR) of up to 114.2 bits/min.

Analysis of QRS loop in the Vectorcardiogram of patients with Chagas' disease.

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p < 0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

Multiparametric detection of epileptic seizures using Empirical Mode Decomposition of EEG records.

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important tool for the diagnosis of epilepsy. In this study, an epileptic seizure classification method based on features of the Empirical Mode Decomposition (EMD) of EEG records is proposed. The Intrinsic Mode Functions (IMFs) of EEG records are first computed, and then several time and frequency features of IMFs are extracted. A features selection based on a Mann-Whitney test and Lambda of Wilks criterion is performed, then these parameters are used in a linear discriminant analysis (LDA) to classify epileptic seizure and normal EEG segments. The algorithm was tested in 3 intracranial channels EEG records acquired in 21 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The signal was divided in 15 s segments. In 45517 segments analyzed (689 with epileptic seizures) the sensitivity and specificity obtained with this method were 69.4% and 69.2% respectively. It could be concluded that the developed method could be a promising tool for epileptic seizure detection in EEG records.

A comparison of monopolar and bipolar EEG recordings for SSVEP detection.

This paper presents a comparative study over the detection of Steady-State Visual Evoked Potential (SSVEP) with monopolar or bipolar electroencephalographic (EEG) recordings in a Brain-Computer Interface experiment. Five subjects participated in this study. They were stimulated with four flickering lights at 13, 14, 15 and 16 Hz and the EEG was measured simultaneously with two bipolar channels (O(1)-P(3) and O(2)-P(4)) and with six monopolar channels at O(1), O(2), P(3), P(4), T(5) and T(6) referenced to F(Z). The EEG was processed by means of spectral analysis and the estimation of power at each stimulation frequency and its harmonics. In average, the monopolar recordings present accuracy in classification of 74.5% against an 80.1% for bipolar recordings. It was found that bipolar recording are better than monopolar recordings for detection of SSVEP.

Analysis of QRS loop changes in the beat-to-beat Vectocardiogram of ischemic patients undergoing PTCA.

In the present work, we have studied dynamic changes of QRS loop in the Vectocardiogram (VCG) of 80 patients that underwent Percutaneous Transluminal Coronary Angioplasty (PTCA). The VCG was obtained for each patient using the XYZ orthogonal leads of their electrocardiographic (ECG) records acquired before, during and after PTCA procedure. In order to analyze the variations of VCG, it has been proposed in this study the following parameters a) Maximum module of the cardiac depolarization vector, b) Volume, c) and Area of vectocardiographic loop corresponding to the QRS complex of each beat, d) Maximum distance between Centroid and the Loop, e) Angle between the XY plane and the Optimum Plane, f) Relation between the Area and Perimeter. The results obtained indicate that the parameters proposed show significant statistics differences (p-value<0.05) before, during (with some exceptions at the first minute of balloon inflation) and after PTCA. We conclude that the variations observed in the proposed parameters correctly represent not only the morphological changes in the depolarization VCG but also they reflect the modifications in the levels of cardiac ischemia induced by PTCA.

An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Sleep apnea detection based on spectral analysis of three ECG - derived respiratory signals.

An apnea detection method based on spectral analysis was used to assess the performance of three ECG derived respiratory (EDR) signals. They were obtained on R wave area (EDR1), heart rate variability (EDR2) and R peak amplitude (EDR3) of ECG record in 8 patients with sleep apnea syndrome. The mean, central, peak and first quartile frequencies were computed from the spectrum every 1 min for each EDR. For each frequency parameter a threshold-based decision was carried out on every 1 min segment of the three EDR, classifying it as 'apnea' when its frequency value was below a determined threshold or as 'not apnea' in other cases. Results indicated that EDR1, based on R wave area has better performance in detecting apnea episodes with values of specificity (Sp) and sensitivity (Se) near 90%; EDR2 showed similar Sp but lower Se (78%); whereas EDR3 based on R peak amplitude did not detect appropriately the apneas episodes reaching Sp and Se values near 60%.

Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification.

In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91+/-5% and 87+/-5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.

An energy-based detection algorithm of epileptic seizures in EEG records.

A simple algorithm to automatically detect segments with epileptic seizures in long EEG records has been developed. The main advantages of the proposed method are: the simple algorithm used and the lower computational cost. The algorithm measures the energy of each EEG channel by a sliding window and calculates some features of each patient signal to detect the epileptic seizure. It is also able to distinguish between seizures and noise artifacts. Nine invasive EEG records acquired by Epilepsy Center of the University Hospital of Freiburg were analyzed in this work. In 90 segments studied (39 with epileptic seizures) the sensitivity obtained with the method is 87.18 %. The algorithm is appropriate to detect epileptic seizures, with high sensitivity, in long EEG records to decrease the time used by physicians and specialists in visual inspections.