Blepharospasm (BL) is characterized by involuntary closures of the eyelids due to spasms of the orbicularis oculi muscle. The gold standard for clinical evaluation of BL involves visual inspection for manual rating scales. This approach is highly subjective and error prone. Unfortunately, there are currently no simple quantitative systems for accurate and objective diagnostics of BL. Here, we introduce a soft, flexible hybrid bioelectronic system that offers highly conformal, gentle lamination on the skin, while enabling wireless, quantitative detection of electrophysiological signals. Computational and experimental studies of soft materials and flexible mechanics provide a set of key fundamental design factors for a low-profile bioelectronic system. The nanomembrane soft electrodes, mounted around the eyes, are capable of accurately measuring clinical symptoms, including the frequency of blinking, the duration of eye closures during spasms, as well as combinations of blinking and spasms. The use of a deep-learning, convolutional neural network, with the bioelectronics offers objective, real-time classification of key pathological features in BL. The wearable bioelectronics outperform the conventional manual clinical rating, as shown by a pilot study with 13 patients. In vivo demonstration of the bioelectronics with these patients indicates the device as an easy-to-use solution for objective quantification of BL.