Devices and methods for stimulation and recording of muscle responses for determining degree of neuromuscular blockade, particularly relevant to elicitation of such responses in those under the influence of anesthesia. A system for estimating the degree of neuromuscular blockade includes at least one stimulating electrode, at least one recording electrode, a pulse generator for providing stimulation to a nerve through the stimulating electrode, and a computing device configured to: apply stimuli to the nerve according to a stimulation protocol, wherein the stimulation protocol provides a plurality of stimulation sequences that vary in frequency of pulses in the stimulation sequence, frequency of the stimulation sequences, number of pulses in the stimulation sequence, or all of the above; measure, by the recording electrode, electrical responses of a muscle; and estimate the degree of neuromuscular blockade based on changes in the electrical responses of the muscle during a stimulation sequence.

Systems and methods are provided for measuring depth, position, and/or angle of a cannula in a medical drug delivery device. In particular, a drug delivery device having a cannula is positioned adjacent to tissue, a voltage pulse is provided to the cannula, a charge is measured at an electrode in the drug delivery device, and the depth of penetration of the cannula is determined based in part on the charge at the first electrode. Systems and methods described herein can be used to determine subcutaneous insertion depth in a wearable bolus injector. In some implementations, insertion depth determination is achieved through capacitive sensors which measure needle depth in a drug delivery device.

A tracer detection device includes an enclosed body, and a plurality of tracer sensors, a battery, a memory, and a transmitter, each disposed within the enclosed body. The plurality of tracer sensors is configured to detect measurement values at a surface and underneath the surface of a gastrointestinal tract. The battery is configured to power the plurality of tracer sensors. The memory is configured to receive measurement values detected by the plurality of tracer sensors. The transmitter is configured to transmit measurement values detected by the plurality of tracer sensors to an external device after the enclosed body has passed through the gastrointestinal tract. The enclosed body includes a steering feature that ensures the enclosed body is oriented in an intended direction. The plurality of tracer sensors triggers release of a drug. The plurality of tracer sensors estimate distances to gastrointestinal walls for normalizing signals.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Systems and methods are provided for evaluating a patient for a neurological movement disorder. A three-dimensional medical image of a brain of the patient is captured and provided to an artificial neural network having at least one convolutional layer to provide a set of output values. The set of output values is provided to a machine learning model to provide a clinical parameter representing one of a presence of the neurological movement disorder in the patient and a response of the patient to a specific treatment for the neurological movement disorder.

An injection sensing device (ISD) (e.g., wearable patch) is paired with an external device (e.g., a medication delivery pen and/or smart phone, iPad, computer) via wireless link or wireline connection. The ISD senses fluctuations in local skin temperature during an injection and provides to the external device captured data from the sensor relating to medicine delivery to a patient to ensure complete delivery and minimize MDD misuse or malfunction or inaccuracies in dosing. The ISD or external device can use captured data and corresponding time stamps to determine flow informatics such as flow rate, total dose delivered, and dose completion status. An LED on the ISD indicates delivery in progress and/or delivery completion.

A garment (e.g., a shirt) for monitoring biometric properties of the wearer of the garment is disclosed. The garment may include sensors for monitoring or assessing the vital signs and body position of the wearer. A processor associated with the garment may provide an output indicative of an assessed condition of the wearer based on the assessed vital signs and the assessed body position of the wearer of the fabric. The garment may include an injector assembly that is used to administer a drug injection to the wearer when the assessed condition indicates that the wearer is in an alert condition.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

An enzymatic sensor configured to determine the concentration of levodopa present in a sample according to a current or a resonant frequency produced in response to levodopa interactions with L-amino acid decarboxylase present in the sensor. A processor associated with the sensor determines levodopa concentration and produces dose recommendation or output according to levodopa concentration.

A system and method for monitoring and delivering medication to a patient includes a controller that has a control algorithm and a closed loop control that monitors the control algorithm. A sensor is in communication with the controller and monitors a medical condition. A rule base application in the controller receives data from the sensor and the closed loop control and compares the data to predetermined medical information to determine the risk of automation of therapy to the patient. The controller then provides a predetermined risk threshold where below the predetermined risk threshold automated closed loop medication therapy is provided. If the predetermined risk threshold is met or exceeded, automated therapy adjustments may not occur and user/clinician intervention is requested.