Cholinergic Crisis with Normal Serum Cholinesterase Levels due to Excessive Galantamine Ingestion: A Case Report.

Galantamine is a cholinesterase inhibitor employed in Alzheimer's disease management. Cholinesterase inhibitors are associated with potential cholinergic side effects that, when severe, can result in cholinergic crises. Although crises induced by other cholinesterase inhibitors, such as distigmine and rivastigmine, have been reported, cases of galantamine-induced cholinergic crises remain undocumented. This study presents a case of cholinergic crisis triggered by galantamine overdose in an 89-year-old woman weighing 37 kg with Alzheimer's disease history, even though her serum cholinesterase levels were normal. The patient overdosed on 264 mg of galantamine, leading to rapid deterioration, marked by restlessness, tremors, sweating, diarrhea, pharyngeal gurgling, and severe hypoxia. Upon arrival at the emergency department, the patient exhibited pinpoint pupils, compromised airway, and low oxygen saturation, necessitating immediate intubation and transfer to the intensive care unit. After 72 h, the patient successfully recovered and was weaned off mechanical ventilation, maintaining normal serum cholinesterase levels. Animal studies suggest a lethal galantamine threshold of 3 to 6 mg/kg in humans. Unlike other cholinesterase inhibitors that typically reduce serum cholinesterase levels during cholinergic crises, galantamine appears to selectively inhibit acetylcholinesterase, possibly sparing butyrylcholinesterase. This selectivity may explain the normal serum cholinesterase levels.

Acute and Prolonged Effects of 300 sec of Static, Dynamic, and Combined Stretching on Flexibility and Muscle Force.

Static stretching (SS), dynamic stretching (DS), and combined stretching (CS; i.e., DS+SS) are commonly performed as warm-up exercises. However, the stretching method with the greatest effect on flexibility and performance remains unclear. This randomized crossover trial examined acute and prolonged effects of SS, DS, and CS on range of motion (ROM), peak passive torque (PPT), passive stiffness, and isometric and concentric muscle forces. Twenty healthy young men performed 300 sec of active SS, DS, or CS (150-sec SS followed by 150-sec DS and 150-sec DS followed by 150-sec SS) of the right knee flexors on four separate days, in random order. Subsequently, we measured ROM, PPT, and passive stiffness during passive knee extension. We also measured maximum voluntary isometric and concentric knee flexion forces and surface electromyographic activities during force measurements immediately before, immediately after, and 20 and 60 min after stretching. All stretching methods significantly increased ROM and PPT, while significantly decreasing isometric knee flexion force (all p < 0.05). These changes lasted 60 min after all stretching methods; the increases in ROM and PPT and the decreases in isometric muscle force were similar. All stretching methods also significantly decreased passive stiffness immediately after stretching (all p < 0.05). Decreases in passive stiffness tended to be longer after CS than after SS or DS. Concentric muscle force was decreased after SS and CS (all p < 0.05). On the other hand, concentric muscle force was unchanged after DS, while the decreases in surface electromyographic activities during concentric force measurements after all stretching methods were similar. Our results suggest that 300 sec of SS, DS, and CS have different acute and prolonged effects on flexibility and muscle force.

Changes in Flexibility and Force are not Different after Static Versus Dynamic Stretching.

In this study, we examined the effects of static and dynamic stretching on range of motion (ROM), passive torque (PT) at pain onset, passive stiffness, and isometric muscle force. We conducted a randomized crossover trial in which 16 healthy young men performed a total of 300 s of active static or dynamic stretching of the right knee flexors on two separate days in random order. To assess the effects of stretching, we measured the ROM, PT at pain onset, passive stiffness during passive knee extension, and maximum voluntary isometric knee flexion force using an isokinetic dynamometer immediately before and after stretching. Both static and dynamic stretching significantly increased the ROM and PT at pain onset (p<0.01) and significantly decreased the passive stiffness and isometric knee flexion force immediately after stretching (p<0.01). However, the magnitude of change did not differ between the two stretching methods for any measurements. Our results suggest that 300 s of either static or dynamic stretching can increase flexibility and decrease isometric muscle force; however, the effects of stretching do not appear to differ between the two stretching methods.

The evaluation of the discriminant ability of multiclass SVM in a study of hand motion recognition by using SEMG.

Electromyogram (EMG) is a kind of biological signal that is generated because of excitement of muscle according to the motor instruction from a brain. We have been experimentally developing the hand motion recognition system by using 4 channels forearm EMG signals. In our system, in order to classify measured EMG SVM (Support Vector Machine) that has higher discriminability is used. Often SVM is used as a non-linear classifier. But, In the conventional system that we developed, we used a canonical discriminant analysis (CDA) method. CDA method is linear discriminant function, but it has shown good experimental results. Therefore, we have compared the discriminant ability between SVM and CDA. In this report, we will describe about the results of this experiment.

Basic study on combined motion estimation using multichannel surface EMG signals.

To improve degree of freedom (DOF) of system control using surface electromyogram (SEMG), we made a basic study of the estimation of user's intended motion including combined motion which is performed by more than one basic motion simultaneously. Our developed system requested to obtain three SEMG characteristics of basic motion and one SEMG characteristics of rest state. This study defines the motion of grasp, supination and pronation as basic motion, and two combined motion which is "grasp +supination" and "grasp + pronation" are set. Our system investigates the possibility of combined motion estimation based on SEMG characteristics of basic motions. Estimation method which is utilizing optimal SEMG that are derived from multichannel SEMG signals is performed by canonical discriminant space and tendency of degree of similarity between combined motion and basic motion. In experimental results, we succeeded in estimation of combined motion although it was included an estimation of basic motions which were constructed elements of combined motion.

Application of least square method for muscular strength estimation in hand motion recognition using surface EMG.

In this study, we describe the application of least square method for muscular strength estimation in hand motion recognition based on surface electromyogram (SEMG). Although the muscular strength can consider the various evaluation methods, a grasp force is applied as an index to evaluate the muscular strength. Today, SEMG, which is measured from skin surface, is widely used as a control signal for many devices. Because, SEMG is one of the most important biological signal in which the human motion intention is directly reflected. And various devices using SEMG are reported by lots of researchers. Those devices which use SEMG as a control signal, we call them SEMG system. In SEMG system, to achieve high accuracy recognition is an important requirement. Conventionally SEMG system mainly focused on how to achieve this objective. Although it is also important to estimate muscular strength of motions, most of them cannot detect power of muscle. The ability to estimate muscular strength is a very important factor to control the SEMG systems. Thus, our objective of this study is to develop the estimation method for muscular strength by application of least square method, and reflecting the result of measured power to the controlled object. Since it was known that SEMG is formed by physiological variations in the state of muscle fiber membranes, it is thought that it can be related with grasp force. We applied to the least-squares method to construct a relationship between SEMG and grasp force. In order to construct an effective evaluation model, four SEMG measurement locations in consideration of individual difference were decided by the Monte Carlo method.

Monte Carlo method for evaluating the effect of surface EMG measurement placement on motion recognition accuracy.

Surface electromyogram (SEMG) is one of the most important biological signal in which the human motion intention is directly reflected. Many systems use SEMG as a source of a control signal. (We call them "SEMG system"). In order to develop SEMG system, constructions of discriminant function and SEMG measurement placement are important factors for accurate recognition. But standard criterions for selection of discriminant function and SEMG measurement placement have not been clearly defined. Almost all of the conventional SEMG system has decided to select measurement placements of SEMG according to standard general anatomical structure of the human body and that mainly focused on signal processing method. However, SEMG measurement placement is also critical for recognition accuracy and evaluating the effect of SEMG measurement placement is important. In this study, we investigate the effect of SEMG measurement placement in hand motion recognition accuracy. We use a 96-channels matrix-type surface multielectrode and four channels are selected as the SEMG measurement placements from the channels that compose multielectrode. 5,000 configurations of SEMG measurement placements are generated by randomly selected number and each configuration is assessed by motion recognition accuracy (i.e. Monte Carlo method). In order to consider the influence of discriminant analysis, our system employs the linear discriminant analysis and nonlinear discriminant analysis. Each selected SEMG measurement placement is evaluated by those two types of discriminant analysis and the results are compared with each other. The experimental results show that motion recognition accuracy differs between these two analyses even if the same SEMG measurement placement is used. Not all optimal measurement placements for linear discriminant function suit for nonlinear discriminant function. The outcome of these investigations, the SEMG measurement placement should be taken into consideration and it suggests the necessity of evaluating the optimal measurement placement depending on a discernment analysis.

Estimation of muscle strength during motion recognition using multichannel surface EMG signals.

The use of kinesiological electromyography is established as an evaluation tool for various kinds of applied research, and surface electromyogram (SEMG) has been widely used as a control source for human interfaces such as in a myoelectric prosthetic hand (we call them 'SEMG interfaces'). It is desirable to be able to control the SEMG interfaces with the same feeling as body movement. The existing SEMG interface mainly focuses on how to achieve accurate recognition of the intended movement. However, detecting muscular strength and reduced number of electrodes are also an important factor in controlling them. Therefore, our objective in this study is the development of and the estimation method for muscular strength that maintains the accuracy of hand motion recognition to reflect the result of measured power in a controlled object. Although the muscular strength can be evaluated by various methods, in this study a grasp force index was applied to evaluate the muscular strength. In order to achieve our objective, we directed our attention to measuring all valuable information for SEMG. This work proposes an application method of two simple linear models, and the selection method of an optimal electrode configuration to use them effectively. Our system required four SEMG measurement electrodes in which locations differed for every subject depending on the individual's characteristics, and those were selected from a 96ch multi electrode using the Monte Carlo method. From the experimental results, the performance in six normal subjects indicated that the recognition rate of four motions were perfect and the grasp force estimated result fit well with the actual measurement result.

Development of the hand motion recognition system based on surface EMG using suitable measurement channels for pattern recognition.

Conventional research on motion recognition using surface electromyogram (SEMG) is mainly focused on how to process with the signals for pattern recognition. However, it is of much consequence to the motion recognition that measurement channels position including useful information about SEMG pattern recognition is selected. In this paper, we present two topics for the hand motion recognition system based on SEMG. First described is the method to select the suitable measurement channels position of multichannel SEMG for the recognition of hand motion, and the second described is an applied systems based on our proposed method. About channel selection, we use a multichannel matrix-type surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those electrodes, system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. The recognition experiments of 18 hand motions show that the average rate was measured to be greater than 96%. And the number of selected channels ranged from 4 to 7. About applied systems, our developed system works as an input interface for the computer (keyboard and pointing device) and a robot hand.

Development of the input equipment for a computer using surface EMG.

SEMG (surface EMG) has many benefits, for example measuring SEMG is easy and a characteristic pattern of SEMG is obtained for each different movement. Therefore, SEMG that is generated by body movement is able to use as a control signal for some electric powered equipments. Our objective is the perfect control of the computer by using SEMG that is generated from forearms. In this paper, we will talk about our developed interface system that works as a keyboard of the computer.

Development of the human interface equipment based on surface EMG employing channel selection method.

In this paper, we describe the human-interface equipment using surface electromyogram (SEMG) based on optimal measurement channels for each subject. In case the SEMG is used as a control signal, individual differences of SEMG are important issue to obtain high accuracy recognition of motions. To solve this problem, we propose a channel selection method of the suitable measurement channels for the recognition of motions. We use a 96-channel matrix-type (6 x 16) surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those 96 electrodes, our system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. Our system generates 10,000 sets of randomly selected channels, and these sets are evaluated by the recognition rate of hand motions. One set that records a highest recognition rate is selected from 10,000 sets for an optimal set of measurement channels. And the one set with the smallest number of measurement channels which fulfil the recognition rate above 90% or the maximum recognition rate above 95% is used for real-time recognition. Six normal subjects were experimentally tested using our system. The recognition rates of 18 hand motions, including 10 finger movements, were assessed for every subject. We were able to distinguish all the motions, and the average recognition rate in the real-time experiment was measured to be greater than 95%. And the number of selected channels ranged from 4 to 7.

A classification method of hand movements using multi channel electrode.

In this study, we describe the classification method of hand movements using 96 channels matrix-type(16times6) of multi channel surface electrode. Today, there are many systems that use the EMG as a control signal. As for those ordinary systems, it has some problem like most of them require the definition of measuring position. We design the new system with multi channel electrode to solve some of those conventional problems. Our system that has 96 channels electrode does not need to select a particular electrode position. Only attaching this electrode, we can obtain correct EMG and this way means providing with a simple and easy way. The purpose of this study is development of the EMG pattern recognition method using multi channel electrode. From measured 96 channels EMG data, we chose one line (16channels) of this electrode with the smallest noise. The EMG signal is recognized by canonical discriminant analysis. In order to recognize the EMG signal, the first three eigenvectors are chosen to form a discriminant space. And Euclidean distance is applied to classify the EMG. From the experiment in this method, we can discriminate 12 movements of the hand including four finger movements. And the recognition rate that can be done in real-time was measured at 80 percent on the average.