Since the late 1980s, additive manufacturing (AM), commonly known as three-dimensional (3D) printing, has been gradually popularized. However, the microstructures fabricated using 3D printing is static. To overcome this challenge, four-dimensional (4D) printing which defined as fabricating a complex spontaneous structure that changes with time respond in an intended manner to external stimuli. 4D printing originates in 3D printing, but beyond 3D printing. Although 4D printing is mainly based on 3D printing and become an branch of additive manufacturing, the fabricated objects are no longer static and can be transformed into complex structures by changing the size, shape, property and functionality under external stimuli, which makes 3D printing alive. Herein, recent major progresses in 4D printing are reviewed, including AM technologies for 4D printing, stimulation method, materials and applications. In addition, the current challenges and future prospects of 4D printing were highlighted.

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To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.

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