This study proposes and clinically tests intramuscular electrical stimulation below motor threshold to achieve prolonged reduction of wrist flexion/extension tremor in Essential Tremor (ET) patients. The developed system consisted of an intramuscular thin-film electrode structure that included both stimulation and electromyography (EMG) recording electrodes, and a control algorithm for the timing of intramuscular stimulation based on EMG (closed-loop stimulation). Data were recorded from nine ET patients with wrist flexion/extension tremor recruited from the Gregorio Marañón Hospital (Madrid, Spain). Patients participated in two experimental sessions comprising: 1) sensory stimulation of wrist flexors/extensors via thin-film multichannel intramuscular electrodes; and 2) surface stimulation of the nerves innervating the same target muscles. For each session, four of these patients underwent random 60-s trials of two stimulation strategies for each target muscle: 1) selective and adaptive timely stimulation (SATS)-based on EMG of the antagonist muscle; and 2) continuous stimulation (CON) of target muscles. Two patients underwent SATS stimulation trials alone while the other three underwent CON stimulation trials alone in each session. Kinematics of wrist, elbow, and shoulder, together with clinical scales, were used to assess tremor before, right after, and 24 h after each session. Intramuscular SATS achieved, on average, 32% acute (during stimulation) tremor reduction on each trial, while continuous stimulation augmented tremorgenic activity. Furthermore, tremor reduction was significantly higher using intramuscular than surface stimulation. Prolonged reduction of tremor amplitude (24 h after the experiment) was

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long shortterm memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexionextension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32-56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion–extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

La aplasia pura de células rojas (APCR) es una anemia aislada secundaria a la insuficiencia de eritropoyesis de patogénesis desconocida. Es un trastorno raro sin preferencia por edad o sexo. Se presenta un caso de APCR secundario al timoma; un tipo de aplasia adquirida que debe sospecharse en casos de anemia normocítica normocrómica con reticulocitopenia. El diagnóstico se confirma por la ausencia de eritroblastos en la médula ósea. El tratamiento consiste en timectomía, y a veces tratamiento corticoide o inmunosupresor. El diagnóstico diferencial de la APCR como complicación hematológica de algunas enfermedades autoinmunitarias incluye a estas.

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